Department of Population - The Republic of the Union of Myanmar … · 2019-02-15 · II Census Report Volume 4-B Mortalit Foreword The 2014 Myanmar Population and Housing Census

The 2014 Myanmar Population and Housing Census

THEMATIC REPORT ON MORTALITYCensus Report Volume 4-B

Department of PopulationMinistry of Labour, Immigration and Population

With technical assistance from UNFPA

SEPTEMBER 2016

The Republic of the Union of Myanmar

Revised second edition, February 2017.

The 2014 Myanmar Populationand Housing Census

THEMATIC REPORT ON MORTALITY

Census Report Volume 4-B

For more information contact:

Department of PopulationMinistry of Labour, Immigration and Population

Office No. 48, Nay Pyi Taw, MYANMAR

Tel: +95 67 431 062www.dop.gov.mm

SEPTEMBER 2016

Census Report Volume 4-B – MortalityII

ForewordThe 2014 Myanmar Population and Housing Census (2014 Census) was conducted with midnight of 29 March 2014 as the reference point. This is the first Census in 30 years; the last was conducted in 1983. Planning and execution of this Census was spearheaded by the former Ministry of Immigration and Population, now the Ministry of Labour, Immigration and Population, on behalf of the Government in accordance with the Population and Housing Census Law, 2013. The main objective of the 2014 Census was to provide the Government and other stakeholders with essential information on the population, in regard to demographic, social and economic characteristics, housing conditions and household amenities. By generating information at all administrative levels, it was also intended to provide a sound basis for evidence-based decision-making and to evaluate the impact of social and economic policies and programmes in the country.

The results of the 2014 Census have been published so far in a number of volumes. The first was the Provisional Results (Census Report Volume 1), released in August 2014. The Census Main Results were launched in May 2015. These included The Union Report (Census Report Volume 2), Highlights of the Main Results (Census Report Volume 2-A), and reports of each of the 15 States and Regions (Census Report Volume 3[A - O]). The reports on Occupation and Industry (Census Report Volume 2-B) and Religion (Census Report Volume 2-C) were launched in March 2016 and July 2016, respectively.

The current set of the 2014 Census publications comprise thirteen thematic reports and a Census Atlas. They address issues on Fertility and Nuptiality; Mortality; Maternal Mortality; Migration and Urbanization; Population Projections; Population Dynamics; the Elderly; Children and Young People; Education; Labour Force Dynamics; Disability; Gender Dimensions; and Housing Conditions, Amenities and Household Assets. Their preparation involved collaborative efforts with both local and international experts as well as various Government Ministries, Departments and research institutions.

Data capture was undertaken using scanning technology. The processes were highly integrated, with tight controls to guarantee accuracy of results. To achieve internal consistency and minimize errors, rigorous data editing, cleaning and validation were carried out to facilitate further analysis of the results. The information presented in these reports is therefore based on more cleaned data sets, and the reader should be aware that there may be some small differences from the results published in the earlier set of volumes. In such instances, the data in the thematic reports should be preferred.

This thematic report presents the status of mortality based on the 2014 Census. The analysis shows that Myanmar has recorded declines in childhood mortality in the last three decades, but mortality rates in the country are still high compared with other countries in the ASEAN region. The decline in mortality rates could be attributed to programmes implemented by the ministry responsible for health. The declines are, however, not evenly distributed across the country. States and Regions such as Ayeyawady, Magway, and Chin, among others, still exhibit high levels of mortality both for children and adults. Life expectancy at birth has increased significantly both for males and females at the Union level, however again there are wide disparities at the subnational levels. There are some States and Regions especially Mon, Yangon and Nay Pyi Taw that have low early-age mortality rates as well as high life expectancy, but in Mon State other development indicators do not support this scenario. In

Census Report Volume 4-B – Mortality III

addition, the wide variation in mortality rates between male and female children is a matter that requires further investigation. There is a need for specialized mortality surveys in such States and Regions to validate findings from the Census.

On behalf of the Government of Myanmar, I wish to thank the teams at the Department of Population, the United Nations Populations Fund (UNFPA) and the authors for their contribution towards the preparation of these thematic reports. I would also like to thank our development partners, namely; Australia, Finland, Germany, Italy, Norway, Sweden, Switzerland, and the United Kingdom for their support to undertake the Census, as well as the technical support provided by the United States of America.

H.E U Thein SweMinister of Labour, Immigration and PopulationThe Republic of the Union of Myanmar

Foreword

Census Report Volume 4-B – Mortality V

Table of Contents

Foreword / IIList of Tables / VIList of Figures / VIList of Appendices / VIIList of Acronyms / VIII

Executive Summary / IX

1. Introduction / 1

2. Early-age mortality / 4

3. Adult mortality and life tables / 18

4. Early-age mortality differentials / 32

5. Spatial distribution analysis of under-five mortality / 38

6. Policy implications / 46

7. Conclusions / 50

References / 52

Glossary of terms and definitions / 56

Appendices / 57

Appendix A. / 58Ever born and non-surviving children by sex, by age of women: numbers and sex ratios, Union, urban and rural areas, State/Region, 2014 Census

Appendix B. / 64The selection of a model life table to estimate indicators of early-age mortality

Appendix C. / 71Infant, child and under-five mortality by Districts

Appendix D. / 78Adjustment of household deaths during the 12 months prior to the Census

Appendix E. / 86Life tables for urban and rural place of residence and for State/Region

Appendix F. / 112Life expectancy at birth by Districts

LIst of Contributors / 115

Census Report Volume 4-B – MortalityVI

List of Tables

List of Figures1 Map of Myanmar by State/Region and District / I

2.1 Mortality questions in the 2014 Census / 5

2.2 Infant mortality trend / 11

2.3 Under-five mortality trend / 11

2.4 Under-five mortality rates by State/Region, 2014 Census / 13

2.5 Under-five mortality rates by State/Region, 2014 Census / 14

2.6 Infant, child and under-five mortality rates by sex, 2014 Census / 16

3.1 Number of survivors [l(x)] by sex in the 12-month period prior to the 2014 Census / 24

3.2 Probability of dying between ages 15 and 59 by State/Region, 2012 / 30

4.1 Infant mortality rate by selected differentials, 2014 Census / 34

5.1 Early-age mortality by Township, 2014 Census / 39

1.1 Mortality indicators, Myanmar (2014 Census), global areas and regions / 2

2.1 Infant, child, and under-five mortality rates, 2014 Census / 7

2.2 Infant and under-five mortality estimates according to different sources / 8

2.3 Infant and under-five mortality trends, 1968 to 2012 from several sources / 10

2.4 Infant, child and under-five mortality rates by urban/rural place of residence and State/Region, 2014 Census / 12

2.5 Infant, child, and under-five mortality by sex, 2014 Census / 15

3.1 Age-sex specific mortality rates, unadjusted and adjusted and smoothed by the Growth Balance Equation method, 2014 Census / 21

3.2 Life table for Myanmar for the 12-month period prior to the 2014 Census / 22

3.3 Selected life table measures, Union, urban/rural areas and State/Region, 2012 / 26

4.1 Infant, child and under-five mortality differentials for selected variables, 2014 Census / 33

5.1 Correlation coefficients between early-age mortality and selected socio-demographic indicators / 41

5.2 Results of a stepwise regression analysis for early-age mortality and selected socio-demographic indicators / 43

Census Report Volume 4-B – Mortality VII

List of Appendices

Appendix A. / 58

Ever born and non-surviving children by sex, by age of women: numbers and sex ratios, Union, urban and rural areas, State/Region, 2014 Census

Appendix B. / 64

The selection of a model life table to estimate indicators of early-age mortality Figure B1 Relation between infant mortality (1q0) and child mortality (4q1) in the nine

families of model life tables and estimates from three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007) / 65

Table B1 Infant and child mortality according to three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007) / 65

Figure B2 Infant and child mortality according to three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007) / 66

Table B2 Indirect estimates of infant, child and under-five mortality rates by sex according to different mortality models, 2014 Census / 68

Appendix C. /71

Infant, child and under-five mortality by DistrictsTable C Infant, child and under-five mortality by State/Region and District, 2014

Census / 73

Appendix D. /78

Adjustment of household deaths during the 12 months prior to the Census Table D1 Unadjusted deaths, death rates and probabilities of dying by sex, 2014 Census / 78

Table D2 Percentage under-enumeration of adult mortality according to the Brass Growth Balance Equation Method, 2014 Census / 80

Table D3 Unadjusted Life Tables based on deaths in the households within 12 months prior to the 2014 Census / 80

Table D4 Unadjusted Life Tables with q(0,1) and q(1,4) estimated indirectly using data on children ever born and non-surviving children, 2014 Census / 82

Table D5 Adjusted Life Tables, 2014 Census / 84

Appendix E. /86

Life Tables for urban and rural place of residence and for State/RegionTable E Life Tables for urban and rural place of residence and for State/Region, 2014

Census / 86

Appendix F. /112

Life expectancy at birth by DistrictsTable F Life expectancy at birth by State/Region and District, 2014 Census / 112

Census Report Volume 4-B – MortalityVIII

List of Acronyms

ASEAN Association of Southeast Asian NationsASFR Age-Specific Fertility RateBGBE Brass Growth Balance EquationCDR Crude Death RateCEB Children Ever BornGDP Gross Domestic ProductGFR General Fertility RateICPD International Conference on Population and DevelopmentIMR Infant Mortality RateLTR Lifetime Risk of Maternal DeathMDGs Millennium Development GoalsMICS Multiple Indicator Cluster SurveyMMRate Maternal Mortality RateMMRatio Maternal Mortality RatioPMFD Proportion of adult female deaths due to maternal causesPPP Purchasing Power ParitySAB Skilled Attendants at BirthSDGs Sustainable Development GoalsTFR Total Fertility RateU5MR Under-Five Mortality RateUN United NationsUNDP United Nations Development ProgrammeUNFPA United Nations Population FundUNICEF United Nations Children’s FundUNPD United Nations Population DivisionUNSD United Nations Statistics DivisionWB World BankWHO World Health Organization

Census Report Volume 4-B – Mortality IX

Executive Summary

The main objectives of this thematic report are to estimate early-age and adult mortality using data from the 2014 Population and Housing Census, and to conduct analyses describing and explaining mortality levels and trends within the socioeconomic context of Myanmar, using appropriate concepts and taking relevant policy issues into consideration.

The vital registration system is not well developed in Myanmar. This situation is common in developing countries and a census is a major alternative source of mortality data in such contexts. Different census data are used to measure early-age and adult mortality. In the 2014 Census, early-age mortality was measured from the responses to two simple retrospective questions on childbearing addressed to ever-married women aged 15 and over. These questions referred to how many live children they had ever given birth to, and how many had died (or survived). Adult mortality was measured by using a question on the number of household members who had died during the 12 months preceding the Census.

According to the 2014 Census, infant and child mortality, which comprises under-five mortality, was high compared to other countries in the region. Previous estimates indicated a rapid decline during the 1960s and 1970s, with a substantial deceleration starting in the early 1980s. The decline has accelerated again during recent years.

An important issue revealed by the data is that substantial differences between sexes were observed in under-five mortality. The probability of dying among males is almost one third higher than that of females. Male infant mortality rates are universally higher than female rates. Biological factors are usually considered responsible for this differential. Nevertheless, child mortality sex differentials tend to disappear or even reverse in most countries. In Myanmar, sex differentials continue to be higher among males during child mortality. This is not easy to explain with census data alone, or even with household survey data. This topic can only be analysed by an in-depth qualitative study.

Adult mortality was found to be high; relatively much higher than under-five mortality. The main reason for this is the particularly high level of male adult mortality. It is probable that these sex differences could be caused by behavioural factors, in particular, the prevalence of unsafe and risky life styles among males. An important related finding is that, contrary to experiences in most countries, male mortality rates in urban areas were higher than in rural areas. However, female mortality rates were lower in urban than in rural areas. Studies that go beyond the interpretation of the 2014 Census data would be necessary to explain these patterns.

In order to better understand under-five mortality levels and patterns, a differential analysis was conducted. Several variables were considered as differentials of under-five mortality rates. All variables selected showed different effects on under-five mortality rates. The most important variable was women’s parity: the higher the number of children already born, the lower the probability of survival of the child. In spite of a low level of fertility in Myanmar, there is still scope to improve infant and child mortality through a decline in fertility. An important decline, especially in infant mortality, would be achieved if women gave birth to fewer children; studies have shown that the fertility rate is directly related to early-age mortality. The other differentials suggest that substantial reductions of under-five mortality

Census Report Volume 4-B – MortalityX

could be achieved by improving the standard of living of the population. A spatial analysis of early-age mortality was conducted. This analysis was conducted using Townships as the unit of analysis, and the proportion of children who had died among those ever born to women aged 20-34 as a mortality indicator. Although substantial variations were found, clusters of Townships with similar mortality rates were revealed. There are two clear clusters of Townships with medium-high and high early-age mortality. The first follows the Ayeyawady River, starting in the north-eastern part of the country and descending south to the delta. The second cluster is in the north-central part of the country and descends towards the first cluster. There are also smaller clusters of low early-age mortality in border areas. The Census does not, however, provide information to show the cause of this spatial distribution of early-age mortality, and an analysis of this type goes beyond the purpose of this thematic report. It would be important, however, to use this information to conduct a study that includes environmental and geopolitical characteristics of the States/Regions.

Twelve variables relating to the characteristics of the population in the Townships were identified. These variables were closely associated with early-age mortality. All the correlations were found to be statistically significant, although the magnitude of some was low.

The only two variables that were found to closely affect early-age mortality were the degree of development or under-development of Townships, and household composition. The other variable was an indicator of fertility. A more equal distribution of early-age mortality rates (and a subsequent overall decline) should be based not on a vertical expansion of health care but in improving the living conditions of the population, in making health care widely accessible, and in better understanding the family role in health care. This last indicator deserves more attention in future studies. In areas where large families prevail, children’s survival probabilities appear to be higher than in places where household extension is limited. In the analysis of mortality differentials, indicators of household extension were also related to under-five mortality. It is important to get a better understanding of the mechanism through which this variable improves survival probabilities.

These results suggest the need for health policies based on the expansion of conventional health services and infrastructure that reach more marginalized populations, especially those living in hard-to reach areas. Policies directed to further reduce fertility may also have an important impact on under-five mortality. These results also call for unconventional propositions such as considering household composition in the formulation of health policies, as well as the importance of interventions that aim to change behaviours for the adoption of more healthy lifestyles, especially among males.

Executive Summary

Census Report Volume 4-B – Mortality 1

Chapter 1. Introduction

In Western Europe, the United States of America and Canada substantial and sustained mortality declines began during the middle of the 19th century, and accelerated continuously until the 1950s. In the 1960s and 1970s, the pace of mortality decline slowed down considerably. Mortality decline in Eastern and Southern Europe began later; between the 1920s and the 1950s. In the developing countries, mortality decline began even later. Some progress took place in a few countries before World War II, but during the three post-war decades mortality levels declined substantially in most of the then Third World countries (UN, 1973; Preston, 1980; Weeks, 2002; Masuy-Stroobant, 2006; Anson and Luy, 2014).

The most remarkable advances were made in the reduction of under-five mortality, measured by the probability of dying between birth and age five, and usually expressed as the number of deaths per 1,000 live births. In spite of this decline, under-five mortality in some parts of the world is still far from reaching levels similar to those observed in the developed countries. For example, between 2010 and 2015 under-five mortality in the developed countries was 6 deaths per 1,000 live births, while in the developing countries it was 54 deaths per 1,000 live births (UNPD, 2015). Gwatkin (1980), in his paper, highlighted the deceleration of under-five mortality decline in several developing countries and raised the question of whether the era of unprecedented rapid mortality declines in these countries is ending. However, a more recent paper (You et al, 2015) shows that under-five mortality rates have continued to decline, especially in East Asia, the Pacific, Latin America and the Caribbean. Nevertheless, the paper concluded that in spite of significant improvements in reducing under-five mortality, more efforts are needed to continue improving child survival in the years to come.

The unprecedented mortality decline experienced by many developing countries just after World War II was mainly the result of the increased availability of health technology. Large-scale programmes for the control of infectious diseases (cholera, measles, smallpox, tuberculosis, yellow fever, etc.), primarily through vaccines, began to be implemented in many developing countries from the late 1940s onwards. The inability of developing countries to reach mortality levels close to those observed in developed countries may be the result of: an inadequate diet and food insecurity; lack of proper sanitation; poor housing; limited knowledge of personal hygiene; harmful traditional health practices; lack of portable water; and also the consequences of the incapacity of health services and programmes to reach the entire population.

Conventional medical interventions, programmes of disease control and the availability of modern health services have reduced mortality substantially in many countries. However, further declines will, largely, only be possible by making the provision of health services far-reaching; and by improving the living standards of large proportions of the population who live in an environment in which major diseases flourish, such as diarrhoea and infections of the respiratory system. Technological medical interventions have not been enough.

In general, mortality indicators point to overall high mortality levels in Myanmar. Table 1.1 shows selected mortality indicators for Myanmar and global areas and regions. Life expectancy at birth is the most frequently used mortality indicator. Its value is four years lower than the average in the developing countries and more than five years lower than the average in the Southeast Asia region.

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Table 1.1 Mortality indicators, Myanmar (2014 Census), global areas and regions

Mortality indicator* Myanmar** Southeast Asia***

Developing countries***

Developed countries***

World***

Crude death rate 9.6 6.9 7.4 10.0 7.8

Life expectancy at birth 64.7 70.3 68.8 78.3 70.5

Male 60.2 67.5 66.9 75.1 68.3

Female 69.3 73.2 70.7 81.5 72.7

Infant mortality rate 62 24 39 5 36

Under-five mortality rate 72 30 54 6 50

*These indicators are:

• Crude death rate (CDR) is the number of deaths that take place in a given year divided by the population in the

middle of that year. It is usually multiplied by 1,000.

• Life expectancy at birth is the average number of years that a newborn baby is expected to live if the mortality

conditions of the year corresponding to the life table remain constant.

• Infant mortality rate (IMR) is the probability of death from birth to age 1.

• Under-five mortality rate (U5MR) is the probability of death from birth to age 5.

Sources:

** The source for Myanmar indicators is the 2014 Census.

*** UN Population Division 2015: http://esa.un.org/unpd/wpp/Download/Standard/Mortality/

Infant mortality in Myanmar, which is one of the most relevant indicators of the health status of the population, is more than two times higher than that observed in Southeast Asia, and almost two times higher than that in the developing countries. During the past century, and especially during the past and present decades, mortality in Myanmar, as shown in this report, has declined substantially. However, as illustrated in Table 1.1, there is much room for improvement, not only through programmes for disease control and access to health services, but also by reducing poverty and improving the living standards of vast sectors of the population.

The 2013 Human Development Index (HDI) ranked Myanmar 150 out of 187 countries with an index value of 0.524. This index varies from 0.337 (Niger) to 0.944 (Norway). The value for East Asia and the Pacific is 0.707 (UNDP, 2014). It is unlikely that mortality in Myanmar, and in particular under-five mortality, can be significantly reduced unless socioeconomic development experiences a substantial and sustained advance.

The main objectives of this report are to estimate early-age and adult mortality using data from the 2014 Census, and to conduct basic analyses to describe and explain mortality levels and trends within the socioeconomic context of the country, using appropriate concepts and taking relevant policy issues into consideration.

It is important to point out that censuses are a useful source of mortality data since they are often the only source that provides total statistical coverage, from the highest level (Union) to the most local areas, in this case Townships. A census can, therefore, make it possible to study all mortality levels and patterns by demographic and socioeconomic groups. The



2014 Census is the only source of mortality data in Myanmar that allows for a particularly satisfactory policy-oriented analysis. In fact, the spatial analysis of mortality that is conducted in this report provides valuable information for the design of policies aimed at reducing mortality, in particular, early-age mortality.

Some populations in three areas of the country were not enumerated. This included an estimate of 1,090,000 persons residing in Rakhine State, 69,800 persons living in Kayin State and 46,600 persons living in Kachin State (see Department of Population, 2015 for the reasons that these populations were not enumerated). In total, therefore, it is estimated that 1,206,400 persons were not enumerated in the Census. The estimated total population of Myanmar on Census Night, both enumerated and non-enumerated, was 51,486,253.

The analysis in this report covers only the enumerated population. It is worth noting that in Rakhine State an estimated 34 per cent of the population were not enumerated as members of some communities were not counted because they were not allowed to self-identify using a name that was not recognized by the Government. The Government made the decision in the interest of security and to avoid the possibility of violence occurring due to inter-communal tension. Consequently, data for Rakhine State, as well as for several Districts and Townships within it, are incomplete, and only represent about two-thirds of the estimated population.

However, basic analyses conducted here indicate that the under-enumeration of certain population groups has only had a limited effect on the measurement of mortality indicators included in this report.



Chapter 2. Early-age mortality

Under-five mortality rates are a leading indicator of the level of child health and overall development in countries. Worldwide, child deaths are falling, but not quickly enough. The global under-five mortality rate has declined by a little more than half, dropping from 91 to 43 deaths per 1,000 live births between 1990 and 2015. However, the current rate of progress was well short of the Millennium Development Goal (MDG) target of a two-thirds reduction by 20151. Under-five mortality remains the focus of the post-2015 MDG agenda, particularly in the newly adopted Sustainable Development Goals (see UN, 2015)2. In Myanmar, between 1981 and 2012, under-five mortality declined by 41 per cent. This figure, which is discussed later in this report, suggests that child survival should receive substantial attention in future health and social policies, and programmes.

Under-five mortality is usually desegregated into infant mortality, which refers to deaths of children between birth and their first birthday, and child mortality, which are deaths between age one and age five. In quantitative terms, infant mortality is the number of infants that die under age one per 1,000 live births in a given year. Child mortality is the number of children that die between one and four years of age per 1,000 live births (Population Reference Bureau, 2011). These three measures are considered as indicators of a conceptual term; early-age mortality. This more generic term is sometimes used interchangeably with under-five mortality.

The vital registration system is not well developed in Myanmar. This situation is common in most developing countries, and thus alternative sources of mortality data have to be used. When this is the case, there are two main approaches to estimate mortality. The first approach is to use demographic surveys with questions that attempt to reconstruct “full birth histories”3. In many cases it is considered that the quality of the information on infant mortality collected through these types of questions is good enough to be used without any further adjustment.

The second approach, called “summary birth histories”, uses census or survey data based on retrospective reporting of childbearing (number of children ever born and number of children surviving). Using sophisticated demographic techniques, called “indirect methods”, it is possible to transform these data into reliable infant and child mortality rates (or probabilities of dying). In these methods the age of the woman is used as an indicator of the age of the children and their exposure time to the risk of dying, and employs models to estimate mortality indicators for periods in the past.

1 In September 2000, world leaders met at the United Nations Headquarters in New York for the United Nations Millennium Declaration. The main outcome of this meeting was the Millennium Development Goals (MDGs), a document containing a set of eight time-bound anti-poverty targets which countries pledged to achieve by 2015. One of these goals, the fourth, was to reduce under-five mortality by two-thirds by 2015.2 The target in this new development agenda is to end preventable deaths of newborns and children aged under five, reduce neonatal mortality to 12 per 1,000 live births, and reduce under-five mortality to 25 per 1,000 live births by 2030.3 In the full birth history, women are asked for the date of birth of each of their children, whether the children are still alive and, if not, their age at death.


The problem is that the two approaches usually give dissimilar results. Typically, full birth histories, based on data collected by demographic surveys, under-estimate early-age mortality while the approach based on indirect methods tends to over-estimate it (see, for example Popoff and Judson (2004)). This issue is discussed in this report, where results from different sources are analysed.

Many censuses include questions to estimate early-age mortality in an indirect way. These are simple retrospective questions on childbearing addressed to all women aged 15 years and over (all ever-married women in the case of the 2014 Myanmar Census). These questions refer to how many live children they have ever given birth to, and how many have died (or survived). The 2014 Census included these questions. The respective data were used to compile statistics on the proportion of children ever born who had died, by the age of their mother. As noted above, these data were transformed, through indirect methods, into infant and child mortality rates.

Figure 2.1 Mortality questions in the 2014 Census

Appendix A contains tables with data on the numbers of children ever born and not surviving by sex and age of women. The information is provided at the Union level, State/Region level and for urban and rural areas.

The most frequently used indirect methods are the Brass-type techniques. William Brass, a British demographer, was the first to develop the procedure for converting the proportion who had died among children ever born, as reported by women (by age groups), into estimates of the probability of dying before attaining certain exact childhood ages (UN, 1983). Afterwards, improved versions were developed. The two main variants are the Trussell and the Palloni-Heligman techniques. Both variants make use of model life tables. The Trussell technique

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23. What work was (Name) mainly doing during the last 12 months? Write detailed work descriptions (for example, Primary teacher, Rice farmer, Taxi driver)

24. What is the major product or service provided in the organisation/enterprise where (Name) mainly worked during the last 12 months? Write detailed descriptions (e.g. Hotel service, Building construction, Garment manufacture)

AGE 10 AND ABOVE AND EMPLOYED

25. Number of children ever born alive(If no children, write “00”)

26. How many of those children are living in this household?

27. How many of those children are living elsewhere (not in this household)?

28. How many of those children are no longer alive (dead)?

Female Female Female

EVER MARRIED WOMEN (AGED 15 AND ABOVE)

Male Male Month YearMale Female Yes

29. Date of last live birth 31. Is thechildstillalive?

NoMale

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Number of children ever born aliveLABOUR FORCEOccupation Industry

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HOUSING CHARACTERISTICS

ElectricityLiquefied Petroleum Gas (LPG)

KeroseneBioGasFirewoodCharcoalCoalStraw/GrassOther

Flush

Water Seal (Improved PL)Pit (Traditional pit latrine)

Bucket (Surface latrine)

OtherNo toilet

Dhani/Theke/In leafBambooEarthWoodCorrugated SheetTile/Brick/ConcreteOther

RadioTelevisionLand line phoneMobile phoneComputer

Internet at home

Car/Pick-up/ Truck/Van

Motorcycle/ Moped/ Tuk TukBicycle4 wheel tractorCanoe/BoatMotor Boat

Cart (Bullock)

CondominiumApartment/Flat

Bungalow/ Brick houseSemi-pacca house

Wooden HouseBambooHut 2-3 yearsHut 1 yearOther

Tap water/PipedTube well, boreholeProtected well/SpringUnprotected well/SpringPool/Pond/LakeRiver/Stream/CanalWaterfall/Rain water

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32. Type of housing unit occupied by this household

33. Type of ownership of housing unit

35. Main source of water for drinking and non-drinking in this household

36. Main type of cooking fuel used in this household

39. Which of the following items does your household have? (mark all that apply)

37. Type of toilet used in this household

38. Main construction material of the housing unit

DrinkingNon-

Drinking

FloorWallRoofYes No Yes No

© DRS Data Services Limited [2013]/O03140813/RLCJ



uses the Coale-Demeny Regional Model Life Tables (West, North, East, and South), and the Palloni-Heligman technique employs the United Nations Model Life Tables for Developing Countries (Latin American, Chilean, South Asian, Far East and General)4.

For the analysis of under-five mortality in this thematic report, the demographic software QFIVE from the demographic package MORTPAK (Version 4.3) was used to estimate infant and child mortality (UNPD, 2013). Because the methods make use of model life tables, it was necessary to establish which model was the most appropriate for the mortality pattern of Myanmar.

The difference between infant and child mortality is one of the most important criteria to consider when selecting the model life table to be used to estimate under-five mortality with a Brass-type technique. The available evidence suggests that for Myanmar, the most suitable model to be used for indirectly estimating under-five mortality is the Chilean Model from the United Nations Life Tables. This model is characterized by a very large difference between infant and child mortality, the former being much larger than the latter. In other words, the under-one component of the early-age mortality rate is very high compared to the one to four-year component. A complete justification for the use of the Chilean Model for estimating early-age mortality indicators is presented in Appendix B.

Table 2.1 shows the results of the early-age, infant and child mortality rates based on the 2014 Census and estimated using the Palloni-Heligman equations and the Chilean Model from the United Nations Model Life Tables. The method estimates mortality rates for several years from 1999 to 20125. Note that unless otherwise indicated, the source for all tables and figures is the 2014 Census. 4 Model life tables are sets of life tables based on the generalization of empirical relationships derived from a group of observed life tables. The Coale-Demeny Regional Model Life Tables and the United Nations Life Tables for Developing Countries are the two main systems of model life tables. These systems are based on empirical life tables that have been developed on the principle of narrowing the selection of a life table to those considered realistic on the basis of examination of mortality levels and patterns calculated for actual populations. These systems cover a wide variety of mortality experiences, so that one may be more appropriate than another for a particular country. Each system has “families” of life tables. The families in the Coale-Demeny system are: East, West, North and South, and the families in the United Nations system are: Latin American, Chilean, South Asian, Far East and General (UN, 1983). 5 The method also provides estimates for the year 2013, but they are usually ignored because they are not reliable. They are based on the experience of the youngest women (aged 15-19) and are highly erratic and frequently show higher mortality than the general trend (although it may be lower in some cases). The second most recent point (age 20-24) may also be out of line regarding the overall trend (Statistical Institute for Asia and the Pacific, 1994). The main source of this pattern is likely to be that children of women aged 15-19 are subject to higher mortality risks. Possible reasons are age itself (physiological immaturity) and also the higher mortality of first births which predominate in this age group. However, more important than age and birth order appears to be the economic and social disadvantages of women that become mothers early in their reproductive years. In fact, unfavorable socioeconomic conditions of mother’s are related with higher probabilities of children dying during infancy. The disproportionate presence of vulnerable women among the 15-19 age group usually results in a strong bias towards over-estimation of mortality during the recent past in this method. This error may extend into the next age group (20-24) affecting the second most recent estimate. The main reasons are that the parity may still be low and mortality easily biased by the proportion of births which occurred when the mother was a teenager. They may also be a result of age heaping at age 20, and general age exaggeration where teenage mothers are declared to be older and are, consequently, placed in the 20-24 age group. By the 25-29 estimates the error is diluted by the prevalence of children born when their mothers were over age 20. There have been several attempts to adjust the mortality rates estimated from the women’s age group 20-24. Nevertheless, in the case of Myanmar, infant and under-five mortality corresponding to children of women aged 20-24 are not out of line and, therefore, the respective rates are not over-estimated as in many countries. This is quite clear in Figures 2.2 and 2.3. For this reason, mortality indicators corresponding to women in the age group 20-24 were included in the analysis without any adjustment.



Table 2.1 Infant, child, and under-five mortality rates, 2014 Census

YearEarly-Age Mortality Rate

Infant Child Under-five

January 2012 61.8 10.0 71.8

June 2010 65.6 11.2 76.8

June 2008 72.7 13.2 85.9

January 2006 80.6 15.7 96.3

April 2003 89.4 18.6 108.0

August 1999 96.6 21.0 117.6

The estimates obtained with this method appear in Table 2.1 as a time series. However, in reality they are not. The estimate corresponding to each year is produced entirely from the data corresponding to the child survival experience of a particular age group of women. For example, the estimate corresponding to January 2012 is produced from the data on children ever born and children surviving among women aged 20-24 at the time of the Census; that corresponding to June 2010 are produced from the data on children ever born and surviving among women aged 25-29 at the time of the Census. The set of points does not strictly represent a time series, but the experience of the respective age group of women at an estimated date. The assumption underlying this is that mortality of children is related solely to the women´s age. In spite of this limitation, the method produces a set of data that can be interpreted as a time series, bearing in mind, however, that it is not and that some characteristics of the trend may be the result of methodological biases.

Other assumptions in the Brass-type methods are that mortality decline has been linear: that the same mortality pattern has been maintained throughout the period under consideration, and that fertility has been constant. All these assumptions are usually relaxed in most countries where the method is utilized, but the technique is, however, quite robust. Changes in mortality patterns and a moderate fertility decline are not very important. Gradually falling fertility will bias the method towards a slight over-estimation of mortality. This is not a serious problem because it usually counteracts latent tendencies for under-estimation (see Statistical Institute for Asia and the Pacific, 1994).

The most recent estimate of under-five mortality corresponds to 2012 and, according to Table 2.1, it is 71.8 deaths per 1,000 live births. For the same date, infant mortality is 61.8 deaths per 1,000 live births and child mortality is 10.0 deaths per 1,000 children surviving between age one and five. As mentioned above, during recent years early-age mortality has experienced a decline in Myanmar: infant mortality declined by 36.0 per cent between 1999 and 2012 from 96.6 to 61.8 deaths per 1,000 live births; child mortality declined by 52.4 per cent from 21.0 to 10.0 deaths per 1,000 children surviving between age one and five; and under-five mortality declined by 38.9 per cent from 117.6 to 71.8 deaths per 1,000 live births in the same period. The values and trends presented in Table 2.1 seem plausible and consistent and, therefore, are unlikely to be affected by the relaxation of the assumptions of the method. However, it is important to compare these estimates with data from other sources, not only for methodological reasons, but also because of substantive considerations.



Table 2.2 shows the seven sources of estimates of early-age mortality indicators available for Myanmar; these sources comprise two censuses6 and five household surveys. The early-age mortality rates that come from surveys are direct estimates that are computed from full birth histories, while those coming from censuses are indirect estimates calculated from information on children ever born and children who have died. The values correspond to the most recent estimate calculated from the respective data collection exercises. The values for under-five mortality show a clear declining trend, except for the Multiple Indicator Cluster Survey (MICS) estimates that indicate an abrupt decline during the second half of the last decade. Infant mortality shows an increase during the second half of the 1990s and, again, the MICS indicates a substantial decline.

Table 2.2 Infant and under-five mortality estimates according to different sources

Sources of under-five mortality data* Estimate period Infant mortalityRate

Under-five mortality

Rate

2014 Census 2012 61.8 71.8

Multiple Indicator Cluster Survey 2009-2010 (MICS) 2005/06-2009/10 37.5 46.1

2007 Fertility and Reproductive Health Survey (FRHS) 2001-2006 68.3 76.7



1991 Population Changes and Fertility Survey (PCFS) 1986-1990 102.9 146.9

1983 Census 1981 100.0 122.2

* Complete information on the sources are shown in the Reference section.

A much better pattern of infant and under-five mortality trends is illustrated in Table 2.3 and Figures 2.2 and 2.3. As is the case in Table 2.2, rates that are derived from surveys are direct estimates, while those which come from censuses are indirect estimations. The years used in Table 2.3 are the mid-year of these three-year periods. The data from MICS was not included in this analysis because the data looks unrealistically low compared with both the 2007 FRHS and the 2014 Census.

The data presented in Figures 2.2 and 2.3 show a general declining trend in both infant and under-five mortality. Several trend lines were evaluated and a third degree polynomial was the one that best fitted the set of points in the scatter gram in both cases. The evaluation was based on the value of the coefficient of determination7. The trend lines, equations and the values of the coefficients are shown in the graphs. The trends, in both infant and under-five mortality, indicate a rapid decline, a clear deceleration which starts by the mid-1980s, and a further decline that has accelerated over recent years.

6 Early-age mortality indicators were estimated from the 1983 census using the same Brass-type method which is applied to the 2014 Census data. 7 This coefficient indicates the degree of fitness of a set of points to an equation line. A coefficient of 1.0 indicates a perfect fit.



It is important to remember that infant and under-five mortality estimates frequently differ between censuses and surveys. As mentioned earlier in this report, surveys usually use full birth histories which tend to under-estimate infant and under-five mortality, while censuses use indirect methods which tend to slightly over-estimate infant and under-five mortality. This is likely to be the situation here. Nevertheless, regardless of the apparent discrepancies, the data in Figures 2.2 and 2.3 indicate a clear trend, which is quite a valuable result. Trend lines are probably not precise, but they seem to describe an approximate general trend accurate enough to be used in other analyses such as population projections. The unexpected decline of infant and under-five mortality rates from the mid-1980s up to recent years needs to be confirmed through further research, hopefully in the immediate future. In this regard, it is important to mention the papers by Gwatkin (1980), who discusses a deceleration of mortality decline in the developing countries in the 1980s, and by You et al (2015) who analyse the more recent decline8.

8 It is important to mention the recently published inter-agency estimates of under-five mortality conducted by UNICEF, WHO, The World Bank and the United Nations (2015). This is a remarkable effort to measure infant and under-five mortality in most countries in the World. The purpose of this project is to measure and project under-five and infant mortality to evaluate progress of the 2015 Millennium Development Goals and the newly adopted Sustainable Development Goals (UN, 2015). In most countries, several available sources were used and sophisticated methods applied in order to establish past trends, present levels and future trends in under-five, infant, and neonatal mortality. In the case of Myanmar, censuses, surveys and vital registration systems were used for the estimates. For 2012, under-five mortality was estimated at 55.3 deaths per 1,000 live births and infant mortality at 43.8 deaths per 1,000 live births. These values are much lower than the estimates obtained from the 2014 Census data (see Table 2.1). The purpose here is not to discuss the methodologies or the reliability of the respective sources of data, but to clarify that this report has a different purpose than the inter-agency project. The objective of this report, as indicated in Chapter 1, is to estimate mortality levels with data from the 2014 Census and conduct basic analyses. Other sources were used to obtain some additional information or to complete some analyses, but the emphasis is on the use of the 2014 Census data. The main purpose of the inter-agency report is to estimate early-age mortality for most countries in the world with a common methodology so as to make valid and reliable comparisons as well as to design global policies. They do not always satisfy the information needs of particular countries. In the case of Myanmar, numerous sources were used, including vital registration systems; the Census data was one among many sources. Estimates for the past and present decade, such as MICS and vital statistics, provided low early-age mortality rates and tended to push down the value of the final estimates. It should also be mentioned that for indirect estimations, the West Model from the Coale-Demeny system was used in the case of the inter-agency estimate. The reliability of the inter-agency estimates may raise objections and be subject to discussion; however, this report is not a place for these discussions. What is important is to point out that the emphasis in this report is on the analysis of mortality based on the 2014 Census data.



Table 2.3 Infant and under-five mortality trends, 1968 to 2012 from several sources

Source Year Infant Under-5

Census 1983 1968.7 147.6 189.7

Census 1983 1972.1 141.6 180.9

Census 1983 1975.0 128.6 162.1

Census 1983 1977.5 119.3 149.1

1991 PCFS 1978.0 101.6 138.2

Census 1983 1979.5 109.3 135.0

Census 1983 1981.1 100.0 122.2

1991 PCFS 1983.0 96.1 126.5

1997 FRHS 1984.0 74.7 107.4

1991 PCFS 1988.0 102.9 146.9

2001 FRHS 1988.0 73.4 103.2

1997 FRHS 1989.0 87.7 125.0

2001 FRHS 1993.0 73.1 98.9

2007 FRHS 1993.5 70.3 85.7

1997 FRHS 1994.0 74.7 105.8

2001 FRHS 1998.0 80.4 95.6

2007 FRHS 1998.5 63.8 77.1

Census 2014 1999.9 96.7 117.7

Census 2014 2003.3 89.5 108.0

2007 FRHS 2003.5 68.3 76.7

Census 2014 2006.1 80.7 96.3

Census 2014 2008.5 72.7 85.9

Census 2014 2010.5 65.6 76.8

Census 2014 2012.0 61.8 71.8



Figure 2.2 Infant mortality trend

0

20

40

60

80

100

120

140

160

180

1960 1970 1980 1990 2000 2010 2020

InfantM

ortalityRa

te

Year

= 2014 Census

y = -0.00245x3 + 14.72x2 - 29,417.3x + 19,601,750.2

R² = 0.84

Figure 2.3 Under-five mortality trend

= 2014 Census

y = -0.00245x3 + 14.71x2 - 29,401.5x + 19,584,313.6

R² = 0.85

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1960 1970 1980 1990 2000 2010 2020

Under-FiveMortalityRa

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Table 2.4 shows infant, child and under-five mortality by State/Region and urban/rural classification. The data clearly indicates that in rural areas early-age mortality levels are higher than in urban areas. Overall, infant mortality is 41.0 deaths per 1,000 live births in urban areas while in rural areas it is 67.2. Under-five mortality rates are 46.3 and 78.8 per 1,000 live births in urban and rural areas, respectively. Figure 2.4 illustrates these differences, and the map at Figure 2.5 shows the spatial distribution of under-five mortality by State and Region.

There are large disparities in early-age mortality rates between States/Regions. For example, infant mortality varies from 86.2 deaths per 1,000 live births in Ayeyawady to less than half this level, 41.8, in Mon. These two States/Regions also exhibit the largest differences in child and under-five mortality. Child mortality is 17.4 per 1,000 live births aged one to four in Ayeyawady and 5.5 in Mon, while under-five mortality is 103.6 per 1,000 live births in Ayeyawady and 47.3 in Mon.

Table 2.4 Infant, child and under-five mortality rates by urban/rural place of residence and State/Region, 2014 Census

Area Infant mortality Rate Child mortality Rate Under-five mortality rate

Union 61.8 10.0 71.8

Urban 41.0 5.3 46.3

Rural 67.2 11.6 78.8

Kachin 52.8 7.8 60.6

Kayah 60.1 9.6 69.7

Kayin 53.6 8.0 61.6

Chin 75.5 14.1 89.6

Sagaing 60.0 9.6 69.6

Tanintharyi 70.8 12.6 83.4

Bago 61.9 10.1 72.0

Magway 83.9 16.7 100.6

Mandalay 50.3 8.1 58.4

Mon 41.9 5.4 47.3

Rakhine 61.1 9.9 71.0

Yangon 44.9 6.1 51.0

Shan 55.5 8.5 64.0

Ayeyawady 86.2 17.4 103.6

Nay Pyi Taw 55.4 8.4 63.8



Figure 2.4 Under-five mortality rates by State/Region, 2014 Census

These variations are likely to be the result of differences in the development of health infrastructures in States/Regions, obstacles to accessing health services, and barriers to benefitting from public health policies and interventions. Other probable factors for the spatial variations are differences in infrastructural development such as improved sources of drinking water, improved sanitation, and communication systems for accessing or transporting food. Particularly important are differences in the socioeconomic characteristics of the population living in these States/Regions, especially women’s education. This issue is further examined in Chapter 4.

It is important to remember that some households were not enumerated in parts of Kachin, Kayin, and Rakhine. However, a very basic examination of the data suggests that the effect of the under-enumeration in these areas is negligible. First of all, infant, child and under-five mortality rates are not outside of the possible range. Rates in Rakhine are similar to the Union levels, and in Kachin and Kayin are lower than the Union level, but still within a reasonable range (see Table 2.4).

71.8

46.3

78.8

60.669.7

61.7

89.8

69.6

83.472.0

100.6

58.347.3

71.0

51.064.0

103.6

63.8

0

20

40

60

80

100

120

StatesandRegions

Under-FiveMortalityRa

teChapter 2. Early-age mortality


Figure 2.5 Under-five mortality rates by State/Region, 2014 Census

There are other indicators that suggest that early-age mortality rates in these three States are not particularly affected by the under-enumeration of certain populations. Sex ratios of children ever born, and sex ratios of children who have died are similar to those observed at the Union level and among other States/Regions (see Appendix A). The same can be said about the relative distribution of the female population by reproductive age groups; it is similar to the national distribution and to that observed among other States/Regions.



The relative distribution of the number of children ever born and children who have died by the age of their mothers is also comparable to the national distribution and to the distributions observed among other States/Regions. These relative distributions are not included in this thematic report, but can be easily calculated using the data in Appendix A. Although these indicators measured in Kachin, Kayin and Rakhine are consistent with those measured at the Union level, further analysis is necessary to determine the effects of under-enumeration on early-age mortality levels and trends.

Infant, child and under-five mortality rates at the District level are presented in Appendix C, Table C. Considering that at the District level the small number of reported deaths may result in random variations, a special methodology was used. This methodology is explained in Appendix C.

Finally, it is important to examine early-age mortality indicators by sex shown in Table 2.5 and Figure 2.6. The huge difference in mortality rates by sex is striking. Under-five mortality among boys is about one third higher than that of girls (81.3 compared to 62.0). A similar trend is observed in the cases of infant and child mortality. While both male and female children have experienced declines in early-age mortality levels, girls have always maintained lower mortality levels than boys. It seems that girls have benefitted more from those factors that have reduced mortality (for an analysis of this topic worldwide see Alkema et al, 2014).

It is important to examine the Census data on children ever born and children who have died. Appendix A shows the respective information at the Union level, State/Region level, and in urban and rural areas. Sex ratios are also shown. There are slightly more male than female births, as indicated by the sex ratio of children ever born (around 104 male births per 100 female births). This pattern is observed in most countries. The ratio of children who have died indicates a substantially higher mortality among male children. At the Union level, the sex ratio is about 130 male deaths per 100 female deaths.

Table 2.5 Infant, child, and under-five mortality by sex, 2014 Census

YearInfant mortality rate Child mortality rate Under-five mortality rate

Both sexes

Males Females Both sexes

Males Females Both sexes

Males Females

January 2012 61.8 69.9 53.6 10.0 11.4 8.4 71.8 81.3 62.0

June 2010 65.6 73.7 57.5 11.2 12.4 9.6 76.8 86.1 67.1

June 2008 72.7 81.3 64.1 13.2 14.4 11.6 85.9 95.7 75.7

January 2006 80.6 89.7 71.7 15.7 16.7 14.0 96.3 106.4 85.7

April 2003 89.4 98.7 80.4 18.6 19.4 17.1 108.0 118.1 97.5

August 1999 96.6 105.8 87.7 21.0 21.6 19.9 117.6 127.4 107.6



Figure 2.6 Infant, child and under-five mortality rates by sex, 2014 Census*

* Note the Figure illustrates only those data in Table 2.5 that relate to January 2012.

This difference in early-age mortality between males and females is also reported by other sources. According to the 2007 FRHS, under-five mortality among boys for the period 1997-2007 was 24 per cent higher than that corresponding to girls (85.0 compared to 68.8). Information on sex preference is not available, but the 2007 FRHS indicates that boys and girls have the same access to immunization and treatment for diarrhoea. Although weak, this indicator suggests equal parental attention and care to boys and girls.

This striking difference in under-five mortality between boys and girls, and the observed recent trends, requires further research. Census data alone cannot sufficiently provide explanations for the reasons behind these disparities. Future demographic and household surveys should include questions that may help to explain this finding; a qualitative study is probably the most suitable research option in this case.

It is pertinent to suggest here possible hypotheses that may guide further research. First of all, it is important to point out that infant mortality is higher in boys than girls in most parts of the world. This has been explained by sex differences in genetic and biological make-up, with boys being biologically weaker and more susceptible to diseases and premature death (Rowland, 2003). This is a possible explanation of the observed sex differences in infant mortality in Myanmar. The main problem is to explain the sex difference in child mortality. In most countries, child mortality sex differences are very small, or in favour of boys.

However, as indicated above, male mortality in Myanmar is much higher than female mortality at these ages. A possible explanation is that in everyday life male children are allowed, or even encouraged, to have greater mobility within the household and its immediate surroundings.

69.9

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62.0

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Male Female

Early

ChildM

ortalityRa

teChapter 2. Early-age mortality


This greater autonomy to explore their environment implies a number of risks that could be life threatening (falls on uneven terrain or stairs, playing with electric appliances, kitchen accidents, wandering on streets with traffic, etc.). On the other hand, it is likely that young girls are more constrained to remain at home, that they are more controlled and encouraged to do activities that require less mobility and, therefore, experience fewer risks. It is important to point out that this explanation is hypothetical; there is no evidence to support it. The verification of this hypothesis requires a qualitative study.



Chapter 3. Adult mortality and life tables

Until recently, in most international forums and among international health and population agencies, adult mortality in the developing countries received little attention as a public health issue. This picture has changed. The high mortality of adults, especially in the African region, is now acknowledged mainly because of the impact of the HIV/AIDS epidemic. However, it has been recognized that in many developing countries, even among those only affected by HIV/AIDS to a small degree, adult mortality could be very high. Several communicable and non-communicable diseases have been identified as having a major impact on adult mortality; malaria, malnutrition, tuberculosis, drug abuse, and mortality resulting from road traffic accidents (Murray, Yang, and Qiao. 1992).

Adult mortality measurement and analysis usually considers the mortality experience of the population aged 15 and over. Measurement of adult mortality in the developing countries has experienced much less attention than the measurement of infant and child mortality. Part of the problem in determining adult mortality arises from the infrequency of adult deaths relative to the size of the population at risk. However, more important in the developing countries is the lack of a reliable civil registration system that records deaths and the demographic characteristics of the deceased. As a consequence, indirect methods of estimation provide the solution to these measurement challenges (UN, 2002). The most frequently used methods to estimate adult mortality indirectly make use of census data. These methods are grouped into census survival methods, growth balance methods, the extinct generation’s method, and estimates derived from information on the survival of parents and on the survival of the spouse (Timæus, Dorrington and Hill, 2013). These methods, except for the extinct generation’s, are used to adjust the data on the number of deaths by age and sex that are collected in censuses using a question on the number of deaths in a household during a fixed period, usually a year. Many censuses include a question to collect such information. Sex and age of the deceased are also recorded. These variables were collected in the 2014 Census of Myanmar. The data obtained from these variables allow for the computing of age-specific mortality rates, which are death rates calculated for specific age groups. These rates can be easily transformed into probabilities of dying at the ages defined by the age group and, in particular, in the indicator of adult mortality, which is the probability of dying between the ages of 15 and 60.

The purpose of estimating adult mortality is to construct life tables. The life table is the most useful demographic tool not only for the study of mortality but also for diverse analytical purposes.

Before moving on to the construction of life tables it is necessary to examine the reliability of mortality data from deaths in a household during a fixed period. Frequently people fail to report a death in a census because of taboos, beliefs, traditions, or emotional reasons. To refer to a recent death may be an emotionally difficult experience. Another problem is that after an adult death a household split may occur and such deaths can remain unreported. These two problems can result in an under-estimation of mortality. However, it may also happen that people confuse a death in a household with a death of a family member living in another household, with the result that the same death is reported more than once. In this case mortality tends to be over-estimated. In addition, errors in the perception of the


12-month period before the census may result in an over- or under-estimation of mortality.

Most of the methods mentioned earlier attempt to solve these over- or under-reporting problems. The methods are usually based on mathematical relationships between the age distribution of deaths and the age distribution of the population. One of these methods, which is easy to apply and conceptually simple, is the Brass Growth Balance Equation (UN, 1983; UN 2002; Dorrington, 2013). It is important to note that this method, as with other similar methods, is used to evaluate adult mortality only, which is usually the population aged 15 and over; however mortality among the population aged 5-9 and 10-14 may also be adjusted using this method. The evaluation of the younger population (those aged under one year and one to four) is calculated using different methods, especially those based on the number of children ever born and surviving (applied in the previous section).

The Brass Growth Balance Equation method (BGBE) relies on comparing age-specific death rates calculated from the number of deaths reported in a census with the death rates implied by the age distribution of the population. This comparison is used to estimate the completeness of the recording of deaths and apply it as an adjustment factor against the reported deaths in the census. This straightforward approach of assessing the completeness of death recording is based on the assumption that the population is “stable”. A stable population has constant fertility and mortality over a long period. The result is a constant rate of population growth and a fixed age composition. Even if the total size of the population is changing, the proportionate structure by age remains constant (for details on the BGBE method, see the three publications cited in the previous paragraph).

It is important, however, to refer to the major limitations of this method. Its main shortfall is the assumption of a stable population. A second assumption that affects its usefulness is that the completeness of deaths recorded should be equal for all ages over a minimum age (usually five years). A third assumption is that the population should be closed to migration. Obviously, there is hardly any population that can meet all these assumptions. However, these assumptions are usually relaxed, to some extent, in most analyses of mortality. What is important is a careful examination of the data to be used and the results obtained. The major question is how much relaxing an assumption will affect the results. There are some procedures in the method that help to overcome the rigidities imposed by the assumptions. For example, the calculations can be limited to the population aged 30 and over in order to avoid the effect of emigration (which usually involves the younger population) or the fact that there has been a rapid fertility decline (the older population has a structure more compatible with a stable population). Also, the oldest age group can be excluded if there is evidence of age exaggeration. In the particular case of Myanmar, after a detailed evaluation of the data and assessment of different results, it was decided to use the age group 40-75 to estimate the extent of the completeness of deaths recorded. Because the age-specific rates can be erratic they need to be graduated (smoothed). This was done by using a demographic method that involves the use of model life tables.

The types of Model Life Tables were explained in Chapter 2. While for early-age mortality estimates the most suitable was the Chilean model, for adult mortality the North Model from the Coale-Demeny family was identified as the most appropriate, for both males and females.



The decision to use the Chilean model to estimate the indicators of early-age mortality was based on the patterns observed in the 2001 and 2009 Fertility and Reproductive Health Surveys. In the case of adult mortality, the North Model was identified by using the program COMPAR from the demographic package MORTPAK (UNPD, 2013). This program compares an observed set of age-specific mortality rates with United Nations and Coale-Demeny model life table patterns, and indicates indices of similarity. This program identified the North pattern from the Coale-Demeny models as the closest to the observed adult mortality pattern in Myanmar. Therefore, the North pattern was used to smooth the age-specific mortality rates calculated from the deaths in households in the twelve months prior to the Census and already adjusted for under-enumeration9.

All the estimates of adult mortality under-enumeration, adjustments and smoothing were undertaken using a special Microsoft Excel spreadsheet that accompanies the UNFPA-IUSSP electronic publication (Moultry et al, 2011). While using this spreadsheet extreme caution was taken regarding the weaknesses of the method, in particular with respect to the relaxation of the assumptions. Several trials were carried out, mainly regarding the age range used to estimate the completeness of deaths recorded. The results were carefully examined.

Table D1 in Appendix D shows the basic Census data on adult mortality and the initial unadjusted age and sex-specific measures. Table D2 shows the percentages of under-enumeration at the Union level, State/Region level and by urban and rural areas, according to sex. The percentage under-enumeration for the Union was 29.7 per cent for males and 37.5 per cent for females. These percentages varied between urban and rural areas and among States/Regions. All age groups were adjusted by the same percentage. An assumption of this method is that misreporting of deaths is the same for all ages over a minimum age. In this case, the minimum age was five years. After adjusting the number of deaths according to these percentages, the data was smoothed using the North Model life table. This operation was repeated for urban and rural areas and for each State/Region. The suitability of the North Model for smoothing the adjusted age-specific mortality rates for urban and rural areas and States/Regions was evaluated in each case. It was found that this pattern (the North Model) was appropriate at the subnational level as well.

Table 3.1 shows the age-sex-specific death rates obtained from the data collected on deaths in households during the 12 months prior to the Census. It also shows the rates adjusted and smoothed with the Brass GBE method. Note that the table does not include the under-five mortality rates. As noted earlier, this method does not provide reliable estimates for the youngest population.

The life table has been one of demography’s most important achievements. In simple words, it examines the effect of mortality on populations by measuring the extent to which death diminishes the numbers of an initial fixed population as it advances through age, following the observed age-specific death rates which are assumed to remain constant. It has several functions or “columns”, each one referring to a different aspect of the mortality level and 9 It is important to mention that the pattern established by the number of deaths in households during the 12 months preceding the Census among the population aged less than 1 year and 1 - 5 years corresponds to the North Model. However, the level, as well as the pattern implied by this data is usually unreliable. The situation is different in the case of adult mortality. Although the level is a problem, the pattern is usually reliable (Timæus, Dorrington, and Hill, 2013).



population pattern (a definition for each column is included as a footnote to Table 3.2). Therefore, by examining a life table a complete representation of the mortality situation of a population can be seen10.

Table 3.1 Age-sex specific mortality rates, unadjusted and adjusted and smoothed by the Growth Balance Equation method, 2014 Census

Age groupUnadjusted Adjusted and smoothed

Male Female Male Female

5 – 9 0.00075 0.00060 0.00097 0.00082

10 – 14 0.00061 0.00048 0.00080 0.00066

15 – 19 0.00114 0.00067 0.00147 0.00092

20 – 24 0.00171 0.00083 0.00222 0.00115

25 – 29 0.00260 0.00099 0.00337 0.00136

30 – 34 0.00394 0.00127 0.00511 0.00175

35 – 39 0.00532 0.00167 0.00690 0.00230

40 – 44 0.00640 0.00218 0.00830 0.00300

45 – 49 0.00766 0.00277 0.00993 0.00381

50 – 54 0.00871 0.00380 0.01290 0.00562

55 – 59 0.01150 0.00541 0.01842 0.00814

60 – 64 0.01535 0.00815 0.02984 0.01352

65 – 69 0.02172 0.01233 0.04857 0.02387

70 – 74 0.03197 0.01955 0.07929 0.04374

75 – 79 0.04736 0.03101 0.12627 0.07944

80+ 0.08063 0.06151 0.22035 0.17553

Table 3.2 shows the life tables constructed with the adjusted and graduated age-sex specific death rates. A definition of each of its functions is provided as a footnote to the table. The software used to calculate life tables in this report is LIFTB from the demographic package MORTPAK (UNPD, 2013). The adult age-sex-specific rates refer to the middle of the period of 12 months before the Census; this corresponds exactly to year 2013.7 (September 2013). The most recent estimate of infant and child mortality was for January 2012 (see Table 2.1 in Chapter 2 and Figure 3.1). Therefore, these rates were extrapolated to year 2013.7 (using an exponential function).

10 It is important to point out that the most common life tables are “period life tables”, which are based on death statistics corresponding to a given year. This type of life table, therefore, represents the mortality experience by the age of a population in a particular year; it does not represent the mortality experience of a real cohort. Instead, it assumes a “hypothetical” or “synthetic” cohort that is subject to the age-specific death rates observed in the particular year. It is important to keep in mind, when interpreting a period life table, that they represent hypothetical cohorts and not real ones. For example, a life expectancy at birth of 60.17 years (see Table 3.2, males) means that a man born in 2013-14 is expected to live to 60.17 years if the mortality conditions of that year remain constant in the future. There are also “generation” or “cohort” life tables that are based on the mortality experience of a real cohort (Rowland, 2003). The construction of this latter life table requires mortality data for several decades.



Table 3.2 Life table for Myanmar for the 12-month period prior to the 2014 Census

Age m(x,n) q(x,n) l(x) d(x,n) L(x,n) S(x,n) T(x) e(x) a(x,n)

Both sexes

0 0.06164 0.05878 100,000 5,878 95,365 0.93903 6,469,721 64.70 0.21

1 0.00248 0.00985 94,122 927 374,148 0.99024 6,374,356 67.72 1.48

5 0.00090 0.00448 93,195 418 464,931 0.99594 6,000,208 64.38 2.50

10 0.00073 0.00364 92,777 338 463,043 0.99542 5,535,278 59.66 2.50

15 0.00119 0.00591 92,440 546 460,923 0.99299 5,072,235 54.87 2.67

20 0.00164 0.00817 91,893 751 457,691 0.99032 4,611,312 50.18 2.63

25 0.00229 0.01141 91,143 1,040 453,261 0.98616 4,153,621 45.57 2.64

30 0.00333 0.01651 90,103 1,488 446,989 0.98074 3,700,360 41.07 2.63

35 0.00445 0.02202 88,615 1,951 438,379 0.97555 3,253,370 36.71 2.59

40 0.00545 0.02687 86,664 2,329 427,663 0.97053 2,814,991 32.48 2.57

45 0.00661 0.03253 84,335 2,743 415,061 0.96244 2,387,329 28.31 2.59

50 0.00892 0.04368 81,592 3,564 399,470 0.94822 1,972,267 24.17 2.62

55 0.01275 0.06190 78,028 4,830 378,786 0.92151 1,572,797 20.16 2.65

60 0.02077 0.09903 73,198 7,249 349,056 0.87370 1,194,011 16.31 2.66

65 0.03447 0.15942 65,949 10,514 304,970 0.79700 844,956 12.81 2.64

70 0.05839 0.25601 55,435 14,192 243,061 0.68211 539,985 9.74 2.60

75 0.09805 0.39416 41,243 16,257 165,795 0.44162 296,924 7.20 2.51

80 0.19055 ... 24,987 24,987 131,129 ... 131,129 5.25 5.25

Males

0 0.07030 0.06671 100,000 6,671 94,892 0.93103 6,017,116 60.17 0.23

1 0.00285 0.01132 93,329 1,056 370,624 0.98868 5,922,224 63.46 1.45

5 0.00097 0.00484 92,273 447 460,246 0.99559 5,551,600 60.17 2.50

10 0.00080 0.00398 91,826 365 458,216 0.99465 5,091,354 55.45 2.50

15 0.00147 0.00734 91,460 671 455,765 0.99088 4,633,138 50.66 2.71

20 0.00222 0.01103 90,789 1,001 451,610 0.98632 4,177,373 46.01 2.67

25 0.00337 0.01674 89,788 1,503 445,432 0.97918 3,725,763 41.50 2.67

30 0.00511 0.02525 88,285 2,229 436,159 0.97035 3,280,331 37.16 2.64

35 0.00690 0.03395 86,055 2,922 423,226 0.96264 2,844,172 33.05 2.59

40 0.00830 0.04066 83,134 3,380 407,417 0.95572 2,420,946 29.12 2.56

45 0.00993 0.04848 79,754 3,866 389,378 0.94523 2,013,529 25.25 2.57

50 0.01290 0.06255 75,887 4,747 368,053 0.92613 1,624,151 21.40 2.60

55 0.01842 0.08825 71,140 6,278 340,863 0.88884 1,256,099 17.66 2.64

60 0.02984 0.13938 64,862 9,041 302,974 0.82515 915,235 14.11 2.64

65 0.04857 0.21750 55,822 12,141 250,000 0.73073 612,261 10.97 2.60

70 0.07929 0.33161 43,681 14,485 182,681 0.60366 362,261 8.29 2.53

75 0.12627 0.47693 29,196 13,924 110,276 0.38592 179,580 6.15 2.44

80 0.22035 ... 15,271 15,271 69,304 ... 69,304 4.54 4.54




Females

0 0.05297 0.05082 100,000 5,082 95,947 0.94730 6,933,432 69.33 0.20

1 0.00204 0.00810 94,918 769 377,705 0.99182 6,837,485 72.04 1.44

5 0.00082 0.00411 94,149 387 469,778 0.99629 6,459,780 68.61 2.50

10 0.00066 0.00331 93,762 310 468,035 0.99615 5,990,002 63.89 2.50

15 0.00092 0.00460 93,452 430 466,233 0.99483 5,521,967 59.09 2.61

20 0.00115 0.00572 93,022 532 463,822 0.99377 5,055,734 54.35 2.58

25 0.00136 0.00680 92,490 629 460,931 0.99232 4,591,912 49.65 2.59

30 0.00175 0.00871 91,861 800 457,389 0.98999 4,130,982 44.97 2.61

35 0.00230 0.01145 91,061 1,043 452,810 0.98689 3,673,593 40.34 2.61

40 0.00300 0.01488 90,018 1,339 446,874 0.98332 3,220,783 35.78 2.60

45 0.00381 0.01886 88,679 1,672 439,418 0.97710 2,773,909 31.28 2.62

50 0.00562 0.02773 87,006 2,413 429,353 0.96676 2,334,491 26.83 2.65

55 0.00814 0.03994 84,594 3,379 415,082 0.94875 1,905,137 22.52 2.67

60 0.01352 0.06554 81,215 5,323 393,810 0.91332 1,490,056 18.35 2.70

65 0.02387 0.11311 75,892 8,584 359,673 0.84878 1,096,245 14.44 2.69

70 0.04374 0.19839 67,308 13,353 305,285 0.74143 736,572 10.94 2.66

75 0.07944 0.33327 53,955 17,981 226,349 0.47518 431,286 7.99 2.59

80 0.17553 ... 35,973 35,973 204,938 ... 204,938 5.70 5.70

Note:

m(x,n) = Age-specific central death rate.

q(x,n) = Probability of dying between exact ages x and x+n (age-specific mortality rate).

l(x) = Number of survivors at age x.

d(x,n) = Number of deaths occurring between ages x and x+n.

L(x,n) = Number of person-years lived between ages x and x+n.

S(x,n) = Survival ratio for persons aged x to x+5 surviving 5 years to ages x+5 to x+10.

T(x) = Number of person-years lived after age x.

e(x) = Life expectancy at age x.

a(x,n) = Average person-years lived by those who die between ages x and age x+n.

The most important function of the life table is to determine life expectancy, and the most important single value is life expectancy at birth. It was estimated that for the 12-month period prior to the Census, life expectancy at birth for males was 60.2 years, while for females it was 69.3 years. Note that life expectancy at birth declines with age; as people get older, they have fewer years ahead of them. For example, women who are aged 25 are expected to live 49.7 more years, while women aged 45 are expected to live 31.3 more years (if the mortality conditions of 2013 to 2014 remain constant in the future). The exception is between ages 0 (life expectancy at birth) and 1; life expectancy actually rises for those children that reach one year of age. This is a bonus for surviving until their first birthday.



Figure 3.1 Number of survivors [l(x)] by sex in the 12-month period prior to the 2014 Census

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Population

AgeMale Female

Appendix D shows the adjusted and unadjusted life tables. It is interesting to observe them to realize the effect of the adjustments. Table D3 shows the life table computed with the data on the number of deaths in households in the 12 months prior to the Census without adjustments. Table D4 shows the life table computed with infant and child mortality estimated indirectly with data on children ever born and children who have died; mortality for ages over five years is calculated with the unadjusted number of deaths in households during the 12 months prior to the Census. Finally, Table D5 presents a life table computed with adjusted data, that is infant and child mortality is estimated indirectly and adult mortality adjusted and smoothed with the Brass GBE method.

The adjusted life table for Myanmar by sex is shown in Table D5. These three tables suggest the need to adjust adult mortality. In fact, life expectancies were too high when unadjusted data were used (Table D3). An independent indication of the under-estimation of mortality is shown in the unadjusted life table. This is obtained from the life expectancies that correspond to the estimates of under-five mortality according to the indirect estimation method. For males, this life expectancy is 63 years and for females it is 71 years. The life tables constructed with the unadjusted data on deaths in households during the 12 months prior to the Census show a life expectancy of 71 years for males and 80 years for females (Table D3).



Life tables for urban and rural areas and for States/Regions are presented in Appendix E. These life tables were calculated in the same way as the Union level life tables, that is, percentages of under-enumeration (see Table D2) were used to adjust the age-specific mortality rates and the North model was applied to smooth the adjusted distribution. Table 3.3 shows a summary of life table measures mainly related to the analysis of adult mortality. In addition to life expectancies at selected ages, the table also contains the conventional measure of adult mortality; the probability of dying between ages 15 and 59 years per 1,000 population. This measure is calculated with the life table function q(x,n) (number of survivors at age x; see footnote at Table 3.2). This measure is preferred because it is computed from life table functions and therefore is more precise and more adequate for comparison than a death rate calculated directly with numbers of deaths and population. The ages were selected because 15 to 59 years is the span of the working age population. Any death in this age span means a loss of a potential producer of economic goods and services. Figure 3.2 shows the spatial distribution of this adult mortality indicator for both sexes.

Table 3.3 provides a great deal of information. Compared to other countries in the region, life expectancy at birth is low in Myanmar (see Table 1.1), and male life expectancy is particularly low. A longer life expectancy for females than males is a universal pattern in contemporary society, but in Myanmar the difference is markedly large. For example, in the developing countries life expectancies at birth are 66.9 and 70.7 years for males and females respectively; the difference is 3.8 years. In Southeast Asia, life expectancies are 67.5 and 73.2 years for males and females respectively, that is a 5.7 years difference. In the developed countries life expectancies are 75.1 and 81.5 years for males and females, respectively, showing a 6.4 years difference (UNSD, 2015). But in Myanmar the difference is 9.1 years (60.2 and 69.3 years)11. Life expectancy at birth is low in Myanmar compared with averages in the developing countries and in Southeast Asia. However, the greatest difference is in respect to male mortality.

Table 2.5 and Figure 2.6, presented in Chapter 2, show large sex differences in under-five mortality. However, low life expectancy of males is only partly due to these young age differences in mortality. As shown in Table 3.3, life expectancy at other selected ages is also much higher for females than for males. The probability of dying between age 15 and 59 among males is more than twice that of females (290.8 compared to 130.9 per 1,000). In summary, mortality among males is much higher than among females not only at the youngest ages, but throughout their entire lifetime.

11 This difference is not unheard of. In the Russian Federation life expectancies at birth of 64.2 years for males and 75.6 years for females corresponds to an 11.4 years difference. Another case is Ukraine with life expectancies at birth of 65.7 for males and 75.7 for females; the difference being 10 years (UNPD, 2015).



Tab

le 3

.3

Sele

cted

life

tab

le m

easu

res,

Uni

on,

urb

an/r

ural

are

as a

nd S

tate

/Reg

ion,

20

12

Are

a

Life

exp

ecta

ncy

at b

irth

Life

exp

ecta

ncy

at a

ge

25Li

fe e

xpec

tanc

y at

ag

e 45

Life

exp

ecta

ncy

at a

ge

65P

rob

abili

ty o

f d

ying

bet

wee

n ag

es 1

5 an

d 5

9 (p

er 1

,000

p

op

ulat

ion)

Mal

esFe

mal

esB

oth

se

xes

Mal

esFe

mal

esB

oth

se

xes

Mal

esFe

mal

esB

oth

se

xes

Mal

esFe

mal

esB

oth

se

xes

Mal

esFe

mal

esB

oth

se

xes

Uni

on

60.1

769

.33

64.7

041

.50

49.6

545

.57

25.2

531

.28

28.3

110

.97

14.4

412

.81

290.

813

0.9

208.

2

Urb

an59

.70

70.9

665

.24

38.6

949

.24

43.9

323

.26

30.7

427

.07

9.78

13.7

911

.92

350.

812

4.4

233.

2

Rur

al60

.72

68.7

964

.73

42.8

049

.68

46.2

726

.16

31.3

728

.81

11.5

214

.59

13.1

526

4.8

134.

219

7.5

Kac

hin

59.3

669

.31

64.2

340

.08

48.8

644

.45

24.7

830

.53

27.8

010

.84

13.9

612

.59

332.

314

1.2

236.

7

Kay

ah59

.10

70.2

264

.28

40.3

050

.50

45.0

924

.61

32.1

228

.17

10.8

715

.28

13.0

333

0.6

128.

823

0.1

Kay

in57

.74

66.7

262

.08

38.5

346

.42

42.3

822

.87

28.7

125

.76

9.90

12.7

611

.39

370.

318

3.6

276.

0

Chi

n57

.37

63.4

960

.48

40.4

945

.59

43.1

425

.10

27.9

926

.57

11.5

412

.82

12.2

033

9.9

218.

927

6.4

Sag

aing

60.9

670

.43

65.7

542

.12

50.6

546

.49

25.8

432

.08

29.0

911

.23

15.0

313

.31

276.

011

8.1

192.

7

Tani

ntha

ryi

62.2

068

.90

65.5

344

.11

49.8

747

.02

27.2

131

.42

29.3

812

.11

14.4

113

.35

237.

112

3.9

179.

5

Bag

o60

.72

69.7

565

.20

42.4

150

.03

46.2

625

.99

31.6

928

.91

11.2

514

.70

13.0

926

8.3

127.

019

4.7

Mag

way

57.0

867

.49

62.2

740

.74

49.7

645

.32

24.6

331

.35

28.0

710

.61

14.4

612

.69

308.

012

8.4

213.

1

Man

dal

ay59

.68

70.1

764

.89

40.1

649

.77

44.9

924

.24

31.3

127

.86

10.4

214

.49

12.6

132

1.2

129.

322

0.7

Mo

n58

.24

69.0

763

.50

37.6

547

.64

46.3

522

.48

29.5

025

.95

9.50

13.0

211

.31

385.

915

3.9

266.

8

Rak

hine

61.6

069

.26

65.4

742

.87

49.7

042

.54

26.2

131

.43

28.8

511

.79

14.8

413

.37

270.

314

0.5

201.

3

Yang

on

60.5

370

.80

65.5

339

.83

49.3

844

.51

23.7

530

.84

27.2

89.

8813

.89

11.9

631

7.7

122.

921

6.9

Sha

n60

.54

69.3

964

.75

41.3

749

.39

45.2

024

.94

31.1

927

.96

11.0

514

.59

12.8

530

1.0

141.

622

1.9

Aye

yaw

ady

60.1

867

.20

63.6

444

.06

49.4

646

.78

26.9

431

.12

29.0

611

.72

14.2

213

.03

227.

813

0.7

178.

1

Nay

Pyi

Taw

63.6

871

.56

67.6

644

.56

51.5

148

.13

27.8

633

.12

30.6

212

.35

15.8

714

.29

222.

411

5.1

167.

0

Sour

ce: C

alcu

late

d f

rom

Tab

le 3

.2, T

able

D5

and

Ap

pen

dix

E.

Cha

pter

3. A

dult

mor

talit

y an

d lif

e ta

bles


The large sex difference in life expectancy at birth is, therefore, probably the combination of an extremely large sex difference at the youngest ages and also large differences at adult ages. Differences in mortality between boys and girls were discussed in Chapter 2. Adult mortality levels and sex differences are now analysed.

The probability of dying between ages 15 and 59 among males is more than twice that of females in Myanmar. In the developing countries, these probabilities are 188 per 1,000 for males and 133 for females (41 per cent higher for males) and in Southeast Asia they are 214 and 131 respectively (63 per cent higher for males). However, in the developed countries the probabilities are 152 for males and 72 for females, that is, the probability of males dying between these ages is more than two times that of females (UNSD, 2015).

In the developed countries adult mortality is much higher for males than females because of biological factors and behavioural differences (Rogers et al, 2010). In general, smoking tobacco and alcohol abuse are more common among men: men also have more road accidents, engage in more dangerous occupations and are more prone to suicide. In addition, men are more reluctant to have regular health check-ups and tend to delay visits to the doctor when disease symptoms surface. In the developed countries more than half of the excess mortality of males is due to two causes: coronary heart diseases and cancer of the lungs, both of which are closely related to lifestyle (Rowland, 2003)12.

In the developing countries, although male adult mortality rates are higher than female rates, the difference tends to be much smaller than in the developed countries. One reason appears to be gender inequality (Medalia and Chang, 2011). This may affect females’ access to health care services and, therefore, counterbalance their biological and behavioural advantages over males. The position of women in the family and in society may also imply living conditions that reduce their life expectancy13. A low educational level may also be an important factor in reducing female life expectancy.

In Myanmar the difference between male and female adult mortality rates is not as high as in the developed countries, but it is much higher than in the developing countries, and also higher than in the Southeast Asia region. Census data does not provide suitable information to analyse this issue. Yet, some hypotheses for further research can be discussed.

The large difference in Myanmar does not seem to be related to socioeconomic development. According to the Human Development Index (UNDP, 2014), Myanmar is ranked 150 among 187 countries in terms of development. During recent decades Myanmar’s index value has improved: from 0.328 in 1980 to 0.524 in 2013, but it is still quite low (the range in 2013 was from 0.337 to 0.944). The GDP per capita is only US$ 3,998. Therefore, the large gap between male and female adult mortality is not consistent with the low degree of development of the country. 12 Research has reported that female hormones provide protection from coronary artery-ischemic heart disease until menopause, resulting in a ten year delay, compared with males, in the onset of heightened deaths from this cause (Rowland, 2003).13 Several studies show that women in the paid labour force have longer life expectancies than housewives. This difference operates through several pathways. Women’s participation in the paid labour force is an indication of their higher educational level, income and an overall lifestyle conducive to better health, and therefore, lower mortality. An interesting explanation is that, spending time outside the home reduces women’s exposure to the harmful indoor air pollution omitted from burning biomass fuel, which contributes to women’s excess morbidity and mortality in the developing countries (Murray and Lopez, 2002).



Gender inequality is also a factor that may increase the difference in male and female adult mortality. Again, the large difference observed in adult mortality between sexes in Myanmar does not appear to be the result of a low level of gender inequality. According to the Gender Inequality Index (UNDP, 2014), Myanmar ranked 83rd among 187 countries with an index value of 0.430 (values range from 0.021 to 0.709)14. Consequently, the large difference between men and women in adult mortality does not appear to be the result of low gender inequalities.

A possible hypothesis is that all those behavioural factors that explain differences in mortality between males and females in the developed countries are exacerbated in Myanmar. In other words, male behaviours that are associated with high mortality risks seem to be deep-rooted in the Myanmar socio-cultural context, more so than in other countries. Some examples of risky behaviours among males in Myanmar are tobacco and alcohol use, deficient self-health care habits and practices, and poor responses to disease symptoms. Motorcycle and motor vehicle accidents are also high in Myanmar, and most of them involve men. Dangerous occupations are another component of an unsafe way of life. Women have adult mortality levels consistent with the degree of the socioeconomic development of the country, but males, plausibly because of behavioural factors, have a higher than expected mortality level. Once again, this issue deserves a further study. Censuses cannot provide the necessary data. An in-depth study is likely to provide the information to test this hypothesis.

Another important piece of information observed in Table 3.3 is the urban/rural difference. Contrary to what may be expected, life expectancy in urban and in rural areas are quite similar. In most countries mortality rates in urban areas are lower than in rural areas, resulting in life expectancy being higher in urban than in rural areas. In the case of Myanmar, infant, child and under-five mortality rates are lower in urban than in rural areas (see Table 2.4 and Figure 2.4 in Chapter 2). However, adults experience fairly equal life expectancies in urban and rural areas. In fact, according to the life expectancy at different ages shown in Table 3.3, adult mortality appears to be higher in urban than in rural areas. This is particularly the case among males. Differences among women are smaller, almost marginal. Regarding the probability of dying between ages 15 and 59, which is the conventional adult mortality indicator, mortality levels in urban areas are higher than in rural areas for males, but not for females.

In this analysis the male-female differences in mortality levels appears again. Census data provides few possibilities to explore these differences. The only characteristics of deceased persons provided by the Census are age and sex. Probable explanations are likely to be related to differences in lifestyle and in health-seeking behaviour. It is possible that males in rural areas are less likely to adopt unsafe behaviours and have a quieter and more peaceful life style. This description of life in rural areas cannot be evaluated in this thematic report, but it is a hypothesis that is worth pursuing in further research.

14 The Gender Inequality Index measures gender inequalities in three important aspects of human development: reproductive health, measured by the maternal mortality ratio and adolescent birth rates; empowerment, measured by the proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 and over with at least some secondary education; and economic status, expressed as labour market participation and measured by labour force participation rates of the female and male populations aged 15 and over.



There could be a second explanation of these differences, which has to do with the classification of rural and urban areas. It is possible that areas classified as “urban” still have rural characteristics, which may have resulted in this sex pattern of adult mortality. An in-depth study of the Census data would be necessary to clarify this issue, but such an analysis is beyond the scope of this thematic report. However, the 2014 Census Thematic Report on Migration and Urbanization (Department of Population, 2016) includes some analyses on this topic.

Table 3.3 also shows differences between States/Regions. Life expectancy at birth for both sexes ranges from 60.5 years in Chin to 67.7 years in Nay Pyi Taw, a difference of more than seven years. In all States/Regions, female life expectancy at birth is higher than that of males but the differences between the States/Regions range from 6.1 years in Chin to 11.1 in Kayah. Similar patterns can be observed in respect to life expectancy at selected ages. For adult mortality, the lowest probability of dying between the ages of 15 and 59 years is also in Nay Pyi Taw (167.0) while the highest is in Chin (276.4). The pattern at the State/Region level is illustrated in the Map at Figure 3.2.



Figure 3.2 Probability of dying between ages 15 and 59 by State/Region, 2012



As for other analyses, the 2014 Census data contains little information to analyse the differences in adult mortality among States/Regions. Yet, a simple approach would be to analyse the relationships between adult mortality rates and selected characteristics of the States/Regions that can be considered indicators of the development or under-development of the State/Region. Four variables for the States/Regions were considered: percentage of the rural population; percentage of households with electricity; percentage of households with a landline, mobile phone or internet at home; and percentage of households with a motor vehicle. The correlation coefficient between each variable and the probability of dying between ages 15 and 59 were calculated15. The values of the coefficients were very low and none of them were statistically significant16 (the significance levels were 0.04; -0.12; -0.23; and 0.09, respectively). The same recommendation mentioned above can be proposed here: in-depth health surveys and qualitative studies are the most adequate strategies to further analyse an explanation of adult mortality.

In the measurement and analysis of adult mortality it is important to bear in mind the under-enumeration of certain populations in three States: Kachin, Kayin, and Rakhine. There is little doubt that this has had an effect on the measurement of adult mortality in these States; but it is difficult to assess the extent of this effect. As indicated earlier, a complete and accurate assessment would be a major undertaking and this is not possible at this early stage of analysis. However, as in the case of early-age mortality, a simple observation of the data suggests that the effect of the under-enumeration of the population in these three States appears to be negligible. A first indication is that life expectancies at birth and at different ages are within possible ranges. In general, values are similar to those calculated at the Union level. Regarding the indicator of adult mortality, probabilities of dying between the ages of 15 and 59 are lowest in Nay Pyi Taw and highest in Chin and Kayin, but still within a reasonable range (see Table 3.3).

There are other indicators that suggest that early-age mortality indicators in these three States are not significantly affected by under-enumeration. The most important is the percentage under-enumeration of deaths (see Appendix D, Table D2). The percentages in Kayin and Rakhine States are, in general, higher than those observed at the Union level, but still within a reasonable range. Another important indicator of the consistency of adult mortality measures in these three States is the age composition of mortality, adjusted and unadjusted. In both cases the compositions follow the national pattern, which corresponds to the North Model (see life tables in Appendix E). These indicators for Kachin, Kayin, and Rakhine are consistent with those measured at the Union level. However further analyses would be necessary to assess the exact effect of under-enumeration on adult mortality levels.

15 This coefficient varies between -1.0 and +1.0. A coefficient of -1.0 indicates a perfect inverse linear relationship, that is, too low values of one variable correspond only to high values of the other and vice versa. A coefficient of +1.0 signifies a perfect positive linear relationship, that is, too high values of one variable correspond only to high values of the other and vice versa. A relationship of 0.0 is a sign of an absence of a relationship

16 Another feature of a correlation coefficient is its “statistical significance”. This concept refers to whether or not a correlation is the result of random factors. This depends on two aspects: the magnitude of the correlation and the sample size or number of cases. A low coefficient might be significant if the number of cases is large, but a comparatively high coefficient might not be significant if the number of cases is small. Statistical significance is tested by using probabilistic distributions that indicate the probability at which a correlation is significant considering its level and number of cases.



Chapter 4. Early-age mortality differentials

In the previous two chapters, sex, urban/rural and State/Region differences in early-age and adult mortality were analysed. Differences, or “differentials”, are also present among groups or segments of the population that are shaped by socioeconomic, cultural and demographic diversities. The purpose of this chapter is to analyse some of these differentials. If the population can be meaningfully divided into subgroups, and if the experience of each subgroup in respect to mortality can be measured separately and the results compared, additional information can be generated, which would be useful in the demographic interpretation of mortality.

Only early-age mortality differentials are considered. Infant, child and under-five mortality rates were estimated using the same Census data and methodology previously used (children ever born and deaths of children by age of women using the Palloni-Heligman variant of the Brass method). Significant adult mortality differentials are not possible to study with the 2014 Census data since, as mentioned earlier, only age and sex were captured among the deceased population. Characteristics of the household where the deceased person lived could be examined. The problem, however, is that indirect calculation of adult mortality rates in the groups established by household variables raises its own complications that are difficult, or even impossible to resolve, such as the small number of cases (deaths) in some categories, and issues with the assumptions on which the method is based.

The following variables regarding infant, child and under-five mortality were considered: (1) characteristics of the mother: mother’s literacy status, mother´s educational level

attainment, mother`s parity; (2) household’s access to water and sanitation facilities: drinking water in the

household, type of toilet in the household; (3) household amenities: availability of electricity in the household, availability of

modern communication devices in the household; (4) household composition: number of adult women (aged 18 and over) living in the

household, number of children (aged less than 10) living in the household; and (5) socioeconomic status of the household: literacy of the head of the household, and

educational level attained by the head of the household.

Table 4.1 presents a summary of the three mortality differentials, while Figure 4.1 illustrates the early-age mortality differentials, in respect to each of these variables.


Table 4.1Infant, child and under-five mortality differentials for selected variables, 2014 Census

Differentials Infant mortality Child mortality Under-five mortality

Union 61.8 10.0 71.8

Mother’s literacy

Literate 58.9 9.3 68.2

Illiterate 76.3 14.2 90.5

Mother’s educational level attained

None 74.9 13.9 88.8

Primary and middle school and vocational training 62.6 10.2 72.8

High school and higher levels 36.6 4.5 41.1

Mother’s parity

1-2 children 30.0 3.5 33.5

3-4 children 92.1 19.4 111.5

5 or more children 139.4 38.4 177.8

Safe drinking water in the mother’s household

Tap water/piped, tube well, borehole, and protected well/spring 57.5 9.0 66.5

All other sources 67.1 11.5 78.6

Type of toilet in the mother’s household

Flush and water seal (improved pit latrine) 52.5 7.7 60.2

All other types 76.7 14.4 91.1

Availability of electricity in the household

Yes 54.2 9.9 64.1

No 74.7 16.3 91.0

Availability of modern communication devices*

Yes 50.1 6.7 56.8

No 75.5 15.0 90.5

Number of adult women in the mother´s household

1 64.3 10.8 75.1

2 55.5 8.4 63.9

3 or more 47.5 6.7 54.2

Number of children in the mother’s household

1 54.3 8.1 62.4

2 41.2 5.4 46.6

3 or more 33.8 4.1 37.9

Literacy of the head of the mother’s household

Literate 60.6 9.8 70.4

Illiterate 68.6 12.0 80.6

Educational level attained by the head of the mother’s household

None 68.4 11.9 80.3

Primary and middle school and vocational training 62.4 10.2 72.6

High school and higher levels 39.3 5.0 44.3

* Includes landline phone, mobile phone and internet at home.

Source: Calculated from special tabulations from the 2014 Census.



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It is observed that all the variables show important differences in infant, child and under-five mortality in respect to the categories in which these variables fall. The most salient differential is parity, that is, the number of times that a woman has given birth. Children of women with higher parities have higher probabilities of dying in early childhood than children of women with lower parities. The probability of dying before his/her fifth birthday of a child whose mother has given birth five or more times, is more than five times higher than the probability corresponding to a child whose mother has given birth to one or two children. Even the probability of a child dying whose mother has given birth to three or four children is more than three times higher than a child whose mother has given birth to just one or two children. This data clearly shows the effect of fertility on infant and child mortality.

This effect has been systematically demonstrated, especially through examination of data from the Demographic and Health Surveys. These surveys provided sound evidence supporting the proposition that pregnancies that occur too early, too late or too frequently have strong effects on under-five mortality, especially on infant mortality (Hobcraft, 1992; Palloni and Rafalimanana, 1997). Some of the mechanisms underlying this relationship are considered to be maternal depletion, child competition for maternal care, household resources, lactation, and crowding. Their role has not been completely explained, but studies in a number of countries with diverse contexts have shown a strong relationship between a mother’s parity (including timing and frequency) and early-age mortality.

Mother’s education differentials are as expected. Illiteracy and low education levels of mothers are associated with high mortality of infants and children. For example, children of mothers with no education have more than double the mortality rate than children of mothers with high school or higher education. This is an important differential because it is related to the capacity of women to raise healthy children which is, in turn, the result of their capability to use health facilities and modern medicines; their knowledge of good practices to maintain their own and their children’s health; and the rejection of harmful traditional practices related to health and diseases. There are several theoretical frameworks that attempt to explain the causes or determinants of these mortality differentials (see, for example, Masuy-Stroobant, 2006).

Other expected differentials are those related to safe drinking water and type of toilet. These are indicators of both the economic situation of a household and the possible exposure of children to infection. The availability of an improved toilet is somewhat more important than safe water for under-five mortality. The probability of dying for children in households with unimproved toilets is 51 per cent higher than among those children living in households with improved toilets (91.1 compared to 60.2). Children in households with unsafe drinking water have a probability of dying which is only 18 per cent higher than those children in households with safe drinking water (78.6 compared to 66.5).

Much more interesting differentials are those referring to household composition. The number of adult women in the household is an indicator of household complexity and extension. The higher the number of women in a household (higher degree of complexity), the higher the possibilities are for children to survive. In fact, under-five mortality rates for children who live in households with three or more women is 54.2, while for those who live



in households with only one woman, the under-five mortality rate is 75.1. It can be proposed, as a hypothetical explanation, that more adult women means more persons are available to take care of, and protect, the child. However, this differential implies a complex and somewhat unexpected relationship. In fact, extended households are usually associated with a traditional family ideology which, in turn, appears to be associated with high fertility levels. If these relationships are true, it follows that parities are likely to be high among women living in extended households. However, as shown above, high parity is directly related to high early-age mortality. Hence, it follows that children of women who live in extended households would likely be high parity children and have less likelihood of survival.

Yet, the data in Table 4.1 shows the opposite: children in households with a large number of women are more likely to survive than children in households with one or two women. The explanation of this differential requires exploring very complex relationships, including other possible intervening variables. The study of these associations goes beyond the scope of this report, but it is worth exploring in the future. Particularly important would be an in-depth analysis of household composition and early-age mortality including interactions with other variables such as fertility, socioeconomic status of the household, level of education of selected members of the household, and mean age of the household. Census data could be particularly suitable for a study of this type. It is important to point out that in spite of being a descriptive and basic analysis, the present results show that household composition is an extremely relevant variable in the analysis of early-age mortality. This is confirmed in some of the following analyses.

The number of children (aged less than 10) in a household was recognized as a differential likely to influence under-five mortality in the same way as parity. More children would result in competition for care and household resources, lactation, and overcrowding. However, the direction of the relationship is the opposite of that expected. The larger the number of children in the household, the better the survival probabilities for children who are under-five. For example, the under-five mortality rate among those who live in households with only one child is 62.4, while for those living in a household with three children or more it is 37.9. The number of children in the household would not be an indicator of fertility, but of household complexity. Large, extended households are likely to have not only a large number of women, but also a larger number of children of different ages that may provide the youngest with a protected and healthy family environment. Again, this result suggests the importance of household composition in early-age mortality and the need to conduct further research in this area. Availability of electricity in the household and access to communication devices indicate mainly the socioeconomic position of the household. Households in isolated and remote areas probably have limited access to these two facilities because the households may be either poor or located in under-developed areas. When electricity is available, under-five mortality is 64.1, when it is not the rate is 91.0. Similarly, when communication devices are available under-five mortality is 56.8, while when communication facilities are not available, the rate is 90.5.



Finally, education of the head of the household is an indicator of socioeconomic status, and it mainly has the same implications as female education. Households with literate and educated heads are more likely to access health care and modern medicines than households that are headed by persons who are illiterate or have little education. Under-five mortality rates in households headed by persons with high school education and above is 44.3, while in households whose heads have no education the rate is 80.3.

In addition to the expected relationships between under-five mortality and variables such as educational level and household sanitation, this analysis has also shown the significance of fertility and the structure of the household. Fertility, in terms of a mother´s parity, is the most important differentiator of under-five mortality. This result suggests the importance of family planning as a major component of any policy directed at reducing infant and child mortality. Also of relevance is the role of household composition, a result that suggests that not only health interventions are relevant for reducing early-age mortality but that social and cultural factors also play a major role.



Chapter 5. Spatial distribution analysis of under-five mortality

Although under-five mortality indicators were presented by States/Regions in Chapter 2 (Table 2.4), it is interesting to expand the spatial analysis using two approaches. First, the mapping of the level of early-age mortality by Township and, second, a regression analysis between mortality and selected socio-demographic variables measured at the Township level.

For this analysis, early-age mortality is measured in a direct way. Brass-type methods to estimate infant and child mortality do not produce reliable results for small areas. Therefore, the percentage of children that died among those who were ever born to women aged 20-34 years was used for this analysis as an indicator of early-age mortality. The disaggregation into infant and child mortality is not possible here because the age at which a child died is not known. This is a very crude measure of early-age mortality, but adequate for this type of analysis since it captures relatively well the mortality differences between Townships. It is important to point out that this analysis assumes that the extent of reporting of children ever born and non-surviving is similar in all Townships. This assumption was kept in mind during this analysis. Townships in Districts in the States of Kachin, Kayin and Rakhine in which there was extensive under-enumeration in the Census were not included in this analysis. The impact of this in the results of this exercise is not possible to assess but, considering previous analysis, it seems that it would have a negligible effect.

Figure 5.1 shows the distribution of early-age mortality by Townships. The values of the variable were divided into four quartile groups. These measures divide the variable into four numerically equal groups, each one including 25 per cent of the Townships. It is important to note that there are two clear geographical clusters of Townships with medium-high and high early-age mortality. The first follows the Ayeyawady River, starting in the northern part of the country and descending south to the delta. The second cluster is in the north-central part of the country and descends towards the first cluster. There are also smaller clusters of low early-age mortality in border areas. The Census does not provide information to study this specific spatial distribution of early-age mortality, and an analysis of this type goes beyond the purpose of this thematic report. It would be important, however, to use this information to conduct a study including environmental and geopolitical characteristics of the States/Regions.

While the objective of mapping early-age mortality is to determine where the Townships with the highest and lowest mortality are located, the objective of the regression analysis is to explain “spatial variations” in early-age mortality between the Townships. In statistical terms, this analysis provides the percentage contribution of a set of variables, called independent variables, to the variance of a dependent variable.


Figure 5.1 Early-age mortality* by Township, 2014 Census

±

Legend

LevelLow

Medium - Low

Medium - High

High

Early-age mortality0 - 5.67

5.67 - 7.16

7.16 - 9.23

9.23 - 19.96

* Early-age mortality is defined in this instance as the percentage of children that died among those who were

ever born to women aged 20-34.



There are, however, two important clarifications to make. Firstly, regression analysis does not show causal relationships between variables. For example, it is not correct to conclude from a regression analysis that illiteracy of mothers is the cause of early-age mortality. Both variables are related in the sense that high levels of early-age mortality are associated with high levels of illiteracy, but this connection cannot be interpreted as a causal relationship.

Secondly, the results of an aggregate level regression analysis cannot be interpreted as relationships at the individual level. For example, areas with high female illiteracy rates usually exhibit higher early-age mortality rates than areas with lower female illiteracy rates. However, from this relationship it is not possible to conclude that children born to women who are illiterate are more likely to die than children born to women who are literate. This is probably correct, as was demonstrated in the differential analysis (see Table 5.1 and Figure 4.1), but a relationship between two aggregate level variables does not necessarily imply that the relationship also takes place among individuals17. It is important, however, to clarify that an aggregate relationship depends on individual relationships and both are linked. Every individual relationship has an aggregate counterpart. However it is important to exercise extreme caution in interpreting results when using correlation and regression at the aggregate level.

Table 5.1 shows the set of independent variables selected for this analysis. It was considered that all these variables may contribute to the explanation of the differences in early-age mortality between Townships. Their possible influence in early-age mortality is quite clear. Some of them were even included in the analysis of differentials. Before conducting the regression analysis it is interesting to examine some of the simple relationships. The correlations between early-age mortality and the set of twelve socio-demographic indicators were calculated18. The results are presented in Table 5.1.

17 The first to call attention to the ecological fallacy was W S Robinson (1950) in a classic paper (see References). 18 The magnitude of the relationships is measured by the Pearson correlation coefficient. This coefficient varies between -1.0 and +1.0. A coefficient of -1.0 indicates a perfect inverse linear relationship, that is, too low values of one variable correspond only to high values of the other and vice versa. A coefficient of +1.0 signifies a perfect positive linear relationship, that is, too high values of one variable correspond only to high values of the other and vice versa. A relationship of 0.0 is a sign of an absence of a relationship. It is quite infrequent to obtain perfect relationships and a coefficient over ± 0.60 is usually considered as high, one from ± 0.30 to ± 0.59 as medium and one from ± 0.10 to ± 0.29 as low. In general coefficients around ± 0.90 are considered very high and those under ± 0.09 as very low.



Table 5.1 Correlation coefficients between early-age mortality* and selected socio-demographic indicators

Variables Correlation coefficient

Statistical significance

Child-woman ratio 0.43 0.000

Percentage of the female population that are illiterate 0.11 0.026

Percentage of the female population with primary education and over -0.13 0.008

Percentage of the population living in urban areas -0.52 0.000

Mean number of adults per household -0.52 0.000

Percentage of the population that is not part of the nuclear household -0.56 0.000

Percentage of the population living in housing units with earth (soil) floors -0.10 0.036

Percentage of the population with access to electricity -0.61 0.000

Percentage of the population with access to safe drinking water -0.38 0.000

Percentage of the population with access to modern communication devices -0.67 0.000

Percentage of the population with access to motor vehicles -0.13 0.011

Percentage of the population with access to improved toilets -0.34 0.000

* Early-age mortality is defined as the percentage of children that died among those who were ever born to

women aged 20-34.


All the variables are significantly related to the dependent variable, at least with an error smaller than 4 per cent19. The magnitude of the relationship, although statistically significant, is low in many cases. According to the magnitude of the coefficients, the most important variables in the explanation of differences in early-age mortality between Townships are: the percentage of the population with access to modern communication devices (-0.67); followed by the percentage of the population with access to electricity (-0.61). The percentage of the population that is not part of the nuclear component of households (-0.56); the mean number of adults per household (-0.52); and the percentage of the population living in urban areas (-0.52) are also variables that exhibit a high correlation with the dependent variable. On the other hand, although statistically significant, some variables are only weakly related: the percentage of the female population that are illiterate (0.11); the percentage of the female population with primary education and over (-0.13); the percentage of housing units with earth (soil) floor (-0.10); and the percentage of the population with access to motor vehicles (-0.13). Note that indicators of the educational level of the female population are weakly related to early-age mortality, when considering that a mother´s education is one of the most ubiquitous determinants of early-age mortality. It is important to remember, however, that this is not an individual level relationship but (as with all the other variables) an indicator that refers to aggregate characteristics.

19 Another feature of a correlation coefficient is its statistical significance. This concept refers to whether or not a correlation is the result of random factors. It depends on two aspects: the magnitude of the correlation and the sample size or number of cases. A low coefficient might be significant if the number of cases is large, but a comparatively high coefficient might not be significant if the number of cases is small. For example, if two variables have been measured at the region level, a correlation of 0.40 may not be significant if the number of regions is only six. However, if the same two variables are measured at a small area level, and there are 600 small areas, a coefficient of 0.40 will certainly be significant. Statistical significance is tested by using probabilistic distributions that indicate the probability at which a correlation is significant considering its level and number of cases. For example a given correlation may be significant at a 20 per cent level. This means that there is a 20 per cent chance that the coefficient is the result of random factors. Usually, the limit to accept a correlation as significant is 1 per cent or 5 per cent. It is important to point out that if a coefficient is not significant at an acceptable level, no matter its value, it cannot be considered as indicative of a relationship. For this analysis, and for the next one, the program SPSS was used. The outputs of the analyses provide the statistical significance of the coefficients.



Table 5.1 describes the “gross” effects of the variation on each independent variable on the variations of the dependent variable. What is important to analyse is the “net” or “partial” effect of each variable on early-age mortality. For example, it could be that early-age mortality is related to the percentage of the population living in urban areas. However, the reason for this relationship could be the fact that urban areas have better sanitary conditions than rural areas. Therefore, it is expected that the percentage of the population with access to safe drinking water and improved sanitation is related to the percentage of the population living in urban areas. If the effect of these two variables is controlled (or maintained/constant), and the relationship between the percentage urban and early-age mortality disappears, then the effect of this variable is spurious, that is, there is no direct effect of the percentage urban in early-age mortality; its only effect occurs through access to safe drinking water and improved sanitation.

Therefore, what is necessary is to investigate the relationship between one independent variable and the dependent variable after controlling the relationships of other variables in the analysis. In other words, the purpose is to analyse the relative importance of a given independent variable in the explanation of the dependent variable when other variables are introduced as controls. The statistical method to conduct this analysis is “multiple regression.”

It is tempting to introduce all the variables in the regression equation. Nevertheless there are several statistical problems when too many interrelated independent variables are considered. This is not a place to explain these issues, but they may distort the result or, at least, make interpretations very difficult. For this reason it is necessary to eliminate some independent variables, in particular those that do not contribute to the explanation of the dependent variable because their partial correlations are too small or, in other words, their net effect is too small. There are several approaches to do this.

For this analysis a “stepwise regression” strategy was utilized20. An initial regression equation is established between one independent variable and the dependent variable. This relationship is the one that has the highest probability of not being caused by random factors (statistically significant). Having established this initial bi-variate regression equation, the method introduces one by one the independent variables not already included in the equation. The selection is done according to the magnitude of the probability that the relationship of the independent with the dependent variable is not caused by random factors. The variable stays if such a probability remains statistically significant. Variables already in the equation are removed if their probability of a non-random relationship with the dependent variable becomes sufficiently low when a new variable is introduced. The process terminates when no more variables are eligible for inclusion or removal. Table 5.2 shows the results of the application of this technique to the variables under analysis.

20 For all the correlation and regression analysis the statistical package SPSS was utilized (version 22).



Table 5.2 Results of a stepwise regression analysis for early-age mortality* and selected socio-demographic indicators

Variables Regression coefficients

Standard error

Statisticalsignificance

Change when the variable is entered into the regression equation

R R2 Standard error

Constant 16.679 1.201 0.000

Percentage of the population with access to modern communication devices

-0.046 0.009 0.000 0.667 0.445 2.17455

Mean number of adults per household -2.829 0.420 0.000 0.708 0.501 2.06494

Child-woman ratio 2.186 0.980 0.026 0.713 0.508 2.05347

Percentage of the population with access to electricity

-0.014 0.007 0.041 0.716 0.513 2.04551

All the variables in the equation 0.732 0.535 2.01796

* Early-age mortality is defined as the percentage of children that died among those who were ever born to

women aged 20-34.


There are four variables that remained in the equation. This means that these variables have significant net or partial effects on the level of early-age mortality in the Townships. The independent variables that are not included in the equation have spurious relationships with early-age mortality, that is, their net effect is not significant. The values presented in Table 5.2 are explained and interpreted below.

The regression coefficients indicate the average change in the mean of the values of the dependent variable if the respective independent variable changes by one unit. For example, if the percentage of the population with access to modern communication devices increases by one unit, that is by 1 per cent, then the average early-age mortality mean will decline by 0.046 per cent (as indicated by the percentage of children of women aged 20-34 who die), after controlling all the other variables. If the mean number of adults per household increases by one unit, that is, by one more adult, early-age mortality will have an average decrease of 2.8 per cent, after controlling all the other variables. The other two coefficients have the same interpretation.

The standard error indicates the average difference between the observed values of the variable and the values obtained by using the equation. It can be said that they are measures of the error in the prediction that would be made if the regression equation is used to forecast early-age mortality. The statistical significance is the probability that the regression coefficient is determined by random factors. As noted in Table 5.2, all of the coefficients have very low probabilities of being randomly caused. The highest correspond to the last variable in the equation, which is 4.1 per cent.

The multiple regression coefficients, symbolized as R, give a more direct interpretation. They indicate the combined effect of all the independent variables on the variations of early-age mortality rates. Its interpretation is the same as the simple correlation coefficient, except that R cannot have negative values. In Table 5.2, the value of R corresponding to the effect



of the four variables in the equation is 0.716, which indicates a medium high joint effect of the four variables in the equation. More useful to interpret the results of the analysis is the coefficient of determination, or R2. Its value, multiplied by 100, indicates the percentage of the variation in early-age mortality among Townships explained by the joint variation of all the independent variables. Thus, the four independent variables explain 51.3 per cent of the variation of early-age mortality among the Townships. The values of R and R2 given in Table 5.2 corresponding to the independent variables indicate the contribution of the respective variables to the explanation of the dependent variable. The R corresponding to the first variable that enters into the regression equation (percentage of the population with access to modern communication devices) is 0.667. This value is equal to the Pearson correlation coefficient. When the second variable is entered (mean number of adults per household), the value of R increases to 0.708. The inclusion of the third variable (child-woman ratio) increases R to 0.713 and the fourth and last variable (percentage of the population in households with access to electricity) increases R to 0.716. The same interpretation is valid for R2. The contribution of each independent variable to the total explanation may seem small, but it is statistically significant and, what is also important, it is a net effect.

As suggested by the analysis of R and R2, the most important variable in this analysis is the percentage of the population with access to modern communication devices. This variable can be considered as an indicator of the degree of development of Townships, their level of poverty and capacity of food security. It may also be an indicator of Townships’ under-development and isolation. This result is not surprising. It is important to recall that this variable was also included in the differential analyses and found to be related to under-five mortality. In other words, this variable affects early-age mortality both at the individual and at the aggregate level.

The second most important variable in the regression analysis is the mean number of adults per household. This variable is an indicator of household complexity or the extension of households which, in turn, is likely to affect the attention that an infant or child receives from their immediate surroundings. Hence, the higher the mean number of adults in households among Townships, the lower the early-age mortality. This relationship is not surprising either. As may be recalled from the differential analysis, the number of women in the household, which is also an indicator of household complexity, was related to under-five mortality. In other words, this result confirms the importance of household composition in the survival of infants and children.

This analysis, however, provides another piece of evidence. The mean number of adults per household and early-age mortality are significantly related even when fertility (measured as child-woman ratio) is controlled. Although these are relationships at the aggregate level, they strongly suggest that fertility does not mediate in the relationship between household composition and early-age mortality. Moreover, this relationship is significant even when some indicators of the levels of development of Townships are controlled (percentage of the population with modern communication devices and percentage of the population with



electricity). To offer possible explanations of these intricate relationships implies long and complex tentative accounts that are unlikely to contribute significantly to the purposes of this thematic report. It is important to highlight that this analysis has drawn attention to the importance of household composition in the analysis of early-age mortality in Myanmar.

The child-woman ratio was the third variable entered in the regression equation. This is an indicator of the level of fertility in the Townships. As may be recalled, women’s parity, also an indicator of fertility, was the main factor in the early-age mortality differential. Therefore, fertility has an effect on early-age mortality at the individual as well as at the aggregate level. In high fertility Townships there are larger numbers of high parity children who have lower survival probabilities. This results in high early-age mortality.

The last variable entered in the regression equation was the percentage of the population with access to electricity. The effect of this variable on early-age mortality appears to be similar to the availability of communication devices. It indicates both the level of socioeconomic development of the Township and its level of isolation and under-development.

As noted in Table 5.2, the set of four independent variables explain 51.3 per cent of the variation of early-age mortality. Although this is important, there is still a 48.7 per cent non-explained variation, which is, not explained by variables that were considered in this study. Evidently, many variables that are relevant in the explanation of early-age mortality were not considered because data were not collected in the 2014 Census. These other variables might be those that are indicators of availability and access to health services and health infrastructure, the physical environment of Townships, and refined indicators of the degree of development of Townships. If such variables were taken into consideration, the percentage of explanation provided by the variables included here would be satisfactory. The analysis indicates that these variables have an important effect in the variations of early-age mortality between Townships regardless of the fact that other variables could also be more important.

The percentage of illiterate women and of women with only primary or no education was not entered in the regression equation. In addition their simple correlations with early-age mortality are quite weak. The same occurs with variables that are indicators of households’ sanitary conditions. Percentages of the population with access to improved toilet facilities and with access to improved drinking water are not in the equation and their simple relationships with early-age mortality are moderate. When analysed as differentials, these two variables were clearly related to under-five mortality. The percentage of the population in housing units with floors made from earth (soil) is also weakly related to early-age mortality; moreover, the relationship is in the opposite direction: the higher the percentage of the population in housing units with earth (soil) floors, the lower the level of early-age mortality. It would be important to analyse the reasons why these variables relate differently at the aggregate and at the individual level. Exploring this issue would demand additional analysis that goes beyond the scope of this thematic report.



Chapter 6. Policy implications

According to the 2014 Census, mortality in Myanmar is high compared with regional and global levels. Therefore, the main implication is the need to expand the provision of health information, services and interventions both spatially and socially. A spatial expansion would mean extending health services to reach the entire population, even those living in the most remote and hard-to-reach areas. A social expansion would consist of increasing health services to reach all social groups regardless of their socioeconomic position, ethnicity, and level of education. However, there are some issues revealed by the analyses that suggest more precise policy implications.

Early-age mortality which comprises infant and child mortality is very high. It declined rapidly during the 1960s and 1970s, but starting in the early 1980s the decline experienced a deceleration. In recent years, a new increase in the pace of decline is taking place. This trend should be confirmed by additional studies and, above all, their determinants should be identified. It is important to undertake further research to understand the factors that affected the slowing of the rapid decline during the 1960s and 1970s, especially for policy purposes.

An important issue is the substantial sex difference that was found in early-age mortality. The probability of mortality among boys is almost one third higher than that of girls. Male infant mortality rates are universally higher than female rates. Biological factors are usually considered responsible for this differential. Nevertheless, in most cases, child mortality sex differentials tend to disappear or even reverse with an increase in age. In Myanmar, sex differentials in mortality continue in the same direction during child mortality. This is not easy to explain with census data, or even with household survey data.

A possible hypothesis is that in Myanmar culture boys are given more freedom to move in and out of the house and immediate surroundings and even encouraged to do so. On the other hand, girls are more restrained, controlled and encouraged to behave more quietly. As a result, boys could be more prone to accidents and more exposed to diseases. If this hypothesis is true, child sex mortality differences are culturally determined. Health policies, therefore, should be directed to modify some parenting practices. However, first of all, research should be conducted in order to verify the proposed hypothesis and identify specific family practices regarding child security and health, as well as understanding the different household health and security environments for boys and girls. Only after an in-depth study can interventions to improve survival probabilities of both male and female children be designed, such as a national campaign to modify some harmful parenting practices, and provide children with a more secure household environment.

It is important to keep in mind that, as indicated above, the spatial and social expansion of the health system is a major undertaking in Myanmar. The Government acknowledges that intensive efforts are required in order to expedite progress towards improving infant and child health (Department of Health, 2014). In addition, as indicated in Chapter 1 in this report, the developing countries have limited possibilities to achieve infant and child mortality rates similar to those prevailing in the developed countries with conventional medical interventions. Substantial declines are possible not only by increasing the provision of health services and ensuring they are far-reaching, but also by improving the standard of living of the population.


Adult mortality is also quite high. As in many developing countries, several diseases such as malaria and tuberculosis have a major impact on adult mortality. Epidemiological studies in Myanmar are noticeably lacking. Census data cannot provide much information on adult mortality except for level and age-sex structure.

As in most countries life expectancy is higher among females. Myanmar is not an exception, but the difference is substantial (9.2 years). This difference is not the result of the previously mentioned sex difference during early years, but a difference prevailing during adult years. A widely used measure of adult mortality is the probability of dying between ages 15 and 59. In Myanmar this probability among males is double that of females. It is also important to consider this differential in urban and rural areas. In urban areas this probability is 2.8 times higher among males than among females, while in rural areas it is 9.7 per cent higher. Male adult mortality (the probability of dying between the ages of 15 and 59) is higher in urban than in rural areas (351 compared to 265). This is contrary to the experience of most countries where mortality in urban areas is lower than in rural areas. Among women, the differential is as expected: higher in rural than in urban areas (134 compared to 124).

As mentioned before, high mortality among males could be related to life style and health care behaviour differences. It is possible that compared to rural males, urban males tend to adopt more unsafe behaviours related to abuse of alcohol, imprudent motor bike driving and poor health seeking behaviour. There is little doubt that medical and health interventions are important and will have an effect on reducing adult mortality, but also campaigns to modify dangerous behaviours may also be relevant. However, it is important to remember that this is just a hypothesis and substantial information should be collected to understand these issues and propose interventions directed to improve male survival probabilities.

Two substantive analyses were conducted on the subject of under-five mortality: an analysis of selected differentials and a spatial multiple regression analysis. Both have important policy implications.

In the differential analysis, several variables were considered as differentiators of under-five mortality rates. All of them demonstrated important mortality differences among the groups that they defined (see Table 4.1 and Figure 4.1). The most important variable was women’s parity: the higher the number of children already born, the lower the probability of survival of the child. In spite of having a low level of fertility, there is still scope to improve infant and child mortality through fertility decline in Myanmar. An important decline, especially in infant mortality, would be achieved if women gave birth to fewer children. The other differentials suggest what has already been discussed: substantial reductions of under-five mortality can be achieved by improving the standard of living of the population (see Table 4.1).

The spatial regression analysis provided a result with important policy implications. This analysis was conducted using Townships as a unit of analysis. The dependent variable was the percentage of children who had died among those who were ever born to women aged 20-34.



Firstly, a map was drawn in order to examine the spatial distribution of early-age mortality (see Figure 5.1). Although substantial variations were found among clusters of Townships with similar mortality rates, it is important to identify the origin of these clusters. The type of analysis required for this purpose goes beyond the scope of this thematic report. Besides, additional information would be necessary for a suitable analysis.

Secondly, twelve variables relating to the characteristics of the population in Townships were prepared. These variables were correlated with the early-age mortality indicator (See Table 5.2). All the correlations were found to be statistically significant, although the magnitude of some of the correlation coefficients was low. After examining the correlations, a multiple regression analysis was conducted. After using a stepwise approach to variable entry into the regression equation, only four out of the twelve variables remained in the regression equation. These variables are the percentage of the population with access to modern communication devices, the mean number of adults per household, the child-woman ratio and the percentage of the population with access to electricity. The most important finding in this analysis is that these four variables explained more than half (51.3 per cent) of the variation in the distribution of early-age mortality rates between Townships.

This is a relatively high proportion of explained variation. Interestingly none of the variables can be considered as indicators of health service provision or health service infrastructure. Two of the variables are indicators of the degree of development of Townships, the other indicates the prevalent household composition, and the fourth variable is an indicator of fertility. It is important that a more equal distribution of early-age mortality rates (and a subsequent overall decline) should be based not only on a vertical expansion of health care, but also in improving the living conditions of the population, in making health care widely accessible, and in better understanding the role of the family in health care. This last indicator deserves more attention in future studies. In areas where large families prevail, children´s survival probabilities appear to be higher than in places where household extension is limited. It is important to remember that in the analysis of differentials, indicators of household extension were also related to early-age mortality. It is important to better understand the mechanism through which this variable improves the survival probabilities of children.

The main policy interventions suggested by the results of the analyses presented in this thematic report can be summarized as follows:

(1) Spatial and social expansion of the health care system so it reaches all areas of the country, regardless of location, and all people, regardless of their socioeconomic status.

(2) Promotion of healthy habits, avoidance of dangerous/risky behaviours, and preventive health care.

(3) Continued and intensified family planning programmes in order to avoid early-age and maternal risks of high parities.

(4) The promotion of good practices within families regarding the upbringing of infants and children.



Taking into consideration that policy interventions should be based on solid information on mortality and its possible determinants, it is also important to summarize the main suggestions for further research proposed in this report:

(1) Analysis of sex differentials in early-age mortality: qualitative studies seem to be the most suitable approach to study this subject.

(2) Identification of the main determinants of adult mortality: this includes an analysis of adult mortality sex differentials. Qualitative studies appear to be the most suitable research strategy.

(3) Analysis of the main determinants of the spatial distribution of early-age mortality: a suitable approach would be to build on the analysis conducted in this thematic report, but include additional variables obtained from administrative statistics.

(4) Analysis of the main differentials and covariates of early-age mortality, in particular those indicators of fertility and family composition: this study can be conducted with data from the 2014 Census, ideally using a multi-level approach that uses aggregate as well as individual level variables.



Chapter 7. Conclusions

The main purpose of a census is to know the number of people living in a given territory. However, censuses also collect diverse information on the characteristics of the population and on vital events that take place among the population. Thus, many modern censuses include questions to estimate mortality. Data used to measure early-age mortality and those to measure adult mortality are collected separately. The former are generally collected with questions referring to women on the number of children ever born and children surviving, and the latter with questions on the members of a household who died during a specified period (usually 12 months) prior to the census.

The mortality data provided by a census are, in general, not as complete and reliable as data obtained with a specialized demographic household survey, but census data on mortality has two advantages. Firstly, there are no sampling concerns, which sometimes may result in serious problems of reliability in survey information. The second advantage is that, although census data on mortality may have some non-sampling errors and other issues, there is sizeable knowledge and experience in the evaluation and adjustment of data on deaths by age and sex to address these problems. Demographic indirect methods can be used to obtain and evaluate under-five and adult mortality indicators. These mortality rates are used to construct the most important mortality analysis instrument: the life table.

Nevertheless it is important to acknowledge that using census data to conduct substantive mortality analyses is limited. For example in the case of adult mortality, only information about the age and sex of the deceased person is collected. However, in spite of this limitation it has been possible to carry out some important analyses presented and discussed in this report. It provides relevant findings, but more in-depth studies are required to unravel the reasons for some of the observed findings.

The main results of the analyses conducted in this thematic report are summarized as follows:

(1) Early-age (infant, child and under-five) and adult mortality rates are still high in Myanmar.

(2) There are substantial mortality differentials in early-age mortality.(3) There is a high level of adult mortality, much higher than that corresponding to

early-age mortality.(4) Large sex differentials in mortality were observed in both the early-age and the

adult population.(5) Male adult mortality rates in urban areas are higher than those in rural areas; but

among females the difference is minimal.(6) Early-age mortality is unevenly distributed in the Union territory, but some clear

clusters can be observed: along the Ayeyawady River, in the central part of the country and in some border areas.

(7) Indicators of the standard of living of the population and the isolation of the place of residence of households are important determinants of infant and child mortality; such relationships were found both at the individual and at the aggregate level.

(8) Fertility was found to be related to early-age mortality both at the individual and at the aggregate level.


(9) Indicators of family composition were found to be important determinants of early-age mortality at the individual and aggregate level.

These topics should be given priority in future mortality studies. However, these results, by themselves, have important policy implications. Some results suggest the expansion of conventional health services and infrastructure to reach marginalized populations, especially those living in hard-to-reach and remote areas. Policies directed to further reduce the fertility rate may also have important effects on under-five mortality. These results also call for unconventional propositions such as considering household composition in the formulation of health policies, as well as the importance of interventions that aim to change behaviours towards more healthy lifestyles, especially among males.

Chapter 7. Conclusions


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Glossary of terms and definitions

Age-specific death rate (ASDR): The total number of deaths per year per 1,000 population of a given age or age group.

Brass Growth Balance Equation: A method to assess the completeness of deaths recorded in a census from the question on the number of deaths in a household during the 12 months preceding the census. It is based on the mathematical relationship between the age distribution of deaths and the age distribution of the population.

Children ever born (CEB): Refers to the lifetime fertility experience of women 15 years and above. It is the total number of children a woman has given birth to in her lifetime.

Child mortality (4q1 ): The probability of a child dying between their first and fifth birthday.

Child mortality rate: The number of children that die between 1 and 4 years of age divided by the number of children alive at age 1, multiplied by 1,000.

Crude death rate (CDR): The number of deaths that take place in a given year divided by the population in the middle of that year. It is usually multiplied by 1,000.

Early-age mortality: This term is used in this report interchangeably with the term Under-five mortality (under-five mortality rate) and refers to all those live births that do not live to exact age five. (Components include neonatal, post neonatal, infant and child mortality). However, only infant, child and under-five mortality indicators are presented in this thematic report.

Infant mortality: Deaths of children before attaining exact age 1.

Infant mortality rate: The number of deaths of infants aged under one year per 1,000 live births (and sometimes referred to as the probability of death from birth to age 1).

Life expectancy: The average number of additional years that a person could expect to live if current mortality levels were to continue for the rest of that person’s life.

Life expectancy at birth: The average number of years that a newborn baby is expected to live if the mortality conditions of the year corresponding to the life table remain constant.

Life table: A tabular display of life expectancy and the probability of dying at each age (or age group) for a given population, according to the age-specific death rates prevailing at that time. The life table gives an organized, complete picture of a population’s mortality.

Under-five mortality rate (U5MR): This is an approximation of the probability of dying before the age of five. It is the number of children who die before reaching five years of age (numerator), divided by the total number of live births in a given one-year period (denominator), multiplied by 1,000.

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Appendix B. The selection of a model life table to estimate indicators of early-age mortality

The most important piece of evidence for the decision to use the Chilean Model was provided by the three Fertility and Reproductive Health Surveys conducted in the country during the past two decades (Department of Population, 1999 and 2004, and Department of Population and UNFPA, 2009).

The graph presented in Figure B1 facilitates the decision regarding which life table model is more suitable for estimated infant and child mortality rates. This graph shows the relationship between infant mortality (1q0) and child mortality (4q1) according to the nine families of model life tables (see footnote 4 in Chapter 2) and the position of the estimates from the three demographic surveys. These surveys collected data on complete birth histories and, therefore, infant and child mortality are estimated for three quinquennial, covering 15 years preceding the date of the survey. Consequently, each survey makes available three estimates of infant and child mortality. In Figure B1, they are represented as dots with a different colour for each survey. The nine lines represent the nine model life tables, four corresponding to the Coale-Demeny system and five to the United Nations system. The three points that represent the estimates from the 2009 survey lay in the line corresponding to the Chilean Model; one point from the 2001 survey also falls in this line. The other two points from the 2001 Survey lay in different lines: South and West Models. The three points corresponding to the 1997 Survey fall in the West model line.

This data suggests a change in the pattern from the West to the Chilean pattern of infant and child mortality. The gap between infant and child mortality has increased mainly because of a decline in child mortality. As indicated above, the Chilean model is characterized by large differences between infant and child mortality, infant being much larger than child mortality. Table B1 and Figure B2 show that the difference between infant and child mortality has, in fact, increased; they also show that this increase has been caused by a more rapid decline of child mortality. The more rapid declining trend of child mortality in respect to infant mortality is clearly shown by the linear trend lines that are drawn in Figure B2 for the sets of infant and child mortality estimates. Table B1 shows the ratios between infant and child mortality. They also indicate an increase in the gap between the two rates. This change through time in the pattern of infant and child mortality has been documented in several countries undergoing a demographic transition (see Zhao, 2007).


Figure B1 Relation between infant mortality (1q0) and child mortality (4q1) in the nine families of model life tables and estimates from three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007)

0.000

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1q0

2007 FRHS

2001 FRHS

1997 FRHS

North

South

East

West

UN-Chilean

UN-FarEastern

UN-General

UN-LatinAmerican

UN-SouthAsian

Table B1 Infant and child mortality according to three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007)

Source Quinquenium Infant mortality (1q0) Child mortality (1q4) Ratio

1997 FRHS 1982-1986 0.0747 0.0353 2.1

2001 FRHS 1986-1990 0.0734 0.0299 2.5

1997 FRHS 1987-1991 0.0877 0.0409 2.1

2001 FRHS 1991-1995 0.0731 0.0258 2.8

2007 FRHS 1991-1996 0.0703 0.0153 4.6

1997 FRHS 1992-1996 0.0747 0.0336 2.2

2001 FRHS 1996-2000 0.0804 0.0151 5.3

2007 FRHS 1996-2001 0.0638 0.0133 4.8

2007 FRHS 2001-2006 0.0683 0.0084 8.1



Figure B2 Infant and child mortality according to three Fertility and Reproductive Health Surveys (FRHS, 1997, 2001, and 2007)

0

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Rates

InfantMortality (1q0) ChildMortality(1q4)

It is relevant to draw attention to the fact that the trends in infant and child mortality are somewhat dispersed around the straight lines, shaping only a general trend. However, this trend is clear enough to argue that a true shift has taken place, in this case, a more rapid decline of child than infant mortality resulting in an increasing gap between the two rates.

Other approaches were used to determine the most suitable life table model to estimate early-age mortality. For example, data from the Census question on the number of deaths during the 12 months preceding the Census is available by age and sex, including those aged less than 1 year and 1-5 years, corresponding to infant and child mortality. The data were examined and the North Model from the Coale-Demeny system was found to be the most suitable for the observed data. This model is characterized by high child mortality as compared to infant mortality. Although this data usually have serious problems of under-enumeration it can capture well the age composition of mortality. However, the exception seems to be for the very young ages, especially infants. It is likely that the under-estimation of infant deaths (less than 1 year) is much higher than that affecting older children and adult deaths. One reason is that babies, who died just after birth, or even a couple of months after birth, would be less likely to be reported by their mothers to the enumerator during the census interview. Although there is no evidence to assess the extent of this problem, it suggests that it could possibly lead to the under-reporting of deceased infants. If deaths of children aged 1-5 are less affected by under-reporting, the resulting pattern is consistent with the North Model. However, the low infant mortality rate in respect to the child mortality rate would be the result of under-enumeration of infants. There are several methods to adjust the number of deaths reported in households during the past 12 months, but none of them provide reliable information on under-five mortality (Timæus, Dorrington, and Hill, 2013).



There is another source of early-age mortality data that was not included in this analysis. It is the 2009-2010 Multiple Indicator Cluster Survey (Ministry of National Planning, Economic Development, Ministry of Health, and UNICEF, 2011). It was considered that this survey under-estimates infant, child and under-five mortality (see Table B2 below). The rates provided by this survey are too low and far from the estimates provided by the FRHS and the 2014 Census. An evaluation of this survey is out of the scope of this thematic report, but the values of the early-age indicators that it shows are too low to be confidently included in this analysis. In addition, this survey also provides three estimates of infant and child mortality. The respective patterns correspond in two cases to the West Model and in one to the South Model.

When there is no evidence regarding which life table model is more suitable for estimating early-age mortality indicators, or when the evidence is confusing, it is suggested that the West Model of the Coale-Demeny system is used (UN, 1983). The reason is that this model corresponds to the most frequent and general mortality patterns. However, in this case, the evidence available was enough to take a decision regarding the most suitable life table.

Epidemiological characteristics of early-age mortality are also relevant in the selection of the model life table. Therefore, in this particular case it would be important to find out the reasons why child mortality declined more rapidly than infant mortality, so that presently the former is much lower compared to the latter. Three explanations can be proposed. The first is that Government health policies favoured younger children more than infants. The second is a decline in fertility, and in particular an increase in birth spacing. Longer birth intervals may have particularly favoured very young children since they would receive their mother’s attention for a longer period of time. The third explanation is that the infant and child mortality trends are the result of methodological problems, such as sample issues. As indicated in Chapter 1, the objectives of this thematic report are somewhat limited and to analyse these explanations is beyond its capacity.

The use of the Chilean model to estimate early-age mortality indicators in a Southeast Asian country may seem a strange choice. The name of the model is given by the region from which the mortality data to construct the model tables was obtained, but it does not mean that its use is restricted to a particular region. A model life table provides an age-sex pattern that can be applied to any country, regardless of the origin of the table (and its name). Only the Chilean Model of the United Nations Life Tables Models exhibits a difference between infant and child mortality large enough to fit the sex differential observed in Myanmar. The East Model from the Coale-Demeny Model Life Tables also exhibits a large difference, but the largest one, corresponding to the Chilean Model is more suitable.

Table B2 shows the estimates of infant, child and under-five mortality according to the nine life table models21. There are significant differences according to the model used for the estimate. These differences are not substantial regarding under-five mortality, but are large regarding infant and child mortality. However, in spite of the differences, in all cases the three early-age mortality indicators are high, regardless of the model life table used for the estimation. It is also important to mention that the large sex difference in infant and child mortality resulting from the use of the Chilean Model is consistent with the differences shown by the corresponding estimates from the 2007 and 2001 FRHS.

21 The software used to compute Table B2 is QFIVE from MORTPAK, Version 4.3 (UNPD, 2013).


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08.5

0.08

585

2008

.70.

0874

320

08.7

0.08

535

2008

.70.

0859

920

08.4

0.08

511

2008

.70.

0840

020

08.3

0.08

471

2008

.50.

0862

2

35 –

39

2006

.50.

0956

720

06.1

0.09

620

2006

.40.

0970

320

06.4

0.09

332

2006

.40.

0948

720

06.1

0.09

223

2006

.40.

0898

420

05.9

0.09

409

2006

.10.

0972

7

40 –

44

2003

.70.

1038

020

03.3

0.10

789

2003

.50.

1084

920

03.7

0.10

129

2003

.70.

1031

520

03.5

0.10

064

2003

.90.

0963

120

03.2

0.10

412

2003

.40.

1078

5

45 -

49

2000

.40.

1133

419

99.9

0.11

751

1999

.90.

1188

620

00.6

0.10

606

2000

.40.

1114

820

00.5

0.10

581

2001

.00.

0993

919

99.9

0.11

159

2000

.30.

1168

0

Cen

sus

Rep

ort

Vo

lum

e 4

-B –

Mo

rtal

ity

69

Ag

e g

roup

Uni

ted

Nat

ions

Mo

del

s (P

allo

ni-H

elig

man

Eq

uati

ons

)C

oal

e-D

emen

y M

od

el (

Trus

sell

Eq

uati

ons

)

Lati

n A

mer

ican

Chi

lean

Sout

h A

sian

Far

Eas

tG

ener

alW

est

No

rth

Eas

tSo

uth

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)

Mal

es

Infa

nt m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

etw

een

ages

0 a

nd 1

): q

(1)

15 -

1920

13.1

0.06

921

2013

.00.

0759

620

13.1

0.06

925

2013

.00.

0688

520

13.1

0.06

900

2013

.30.

0737

120

13.3

0.07

229

2013

.30.

0738

020

13.3

0.07

027

20 –

24

2012

.10.

0628

820

12.0

0.06

994

2012

.10.

0624

420

12.0

0.06

307

2012

.10.

0638

320

12.2

0.06

527

2012

.20.

0600

320

12.1

0.06

836

2012

.20.

0663

3

25 -

29

2010

.70.

0644

320

10.5

0.07

373

2010

.60.

0639

420

10.6

0.06

504

2010

.70.

0658

520

10.5

0.06

605

2010

.60.

0591

120

10.4

0.07

111

2010

.50.

0688

8

30 –

34

2008

.80.

0694

620

08.5

0.08

131

2008

.70.

0691

420

08.7

0.06

970

2008

.70.

0712

020

08.4

0.07

087

2008

.70.

0626

120

08.3

0.07

745

2008

.50.

0747

9

35 –

39

2006

.50.

0751

520

06.1

0.08

961

2006

.30.

0751

020

06.4

0.07

448

2006

.40.

0773

320

06.1

0.07

592

2006

.40.

0660

520

05.9

0.08

446

2006

.10.

0817

6

40 –

44

2003

.70.

0800

020

03.3

0.09

863

2003

.50.

0820

420

03.7

0.07

895

2003

.70.

0825

820

03.5

0.08

171

2003

.90.

0699

020

03.2

0.09

190

2003

.40.

0878

6

45 -

49

2000

.30.

0857

019

99.9

0.10

576

1999

.90.

0884

420

00.6

0.08

213

2000

.40.

0880

120

00.5

0.08

530

2001

.00.

0713

319

99.9

0.09

715

2000

.30.

0927

5

Chi

ld m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

etw

een

ages

1 a

nd 5

): q

(1,4

)

15 -

1920

13.1

0.02

935

2013

.00.

0140

020

13.1

0.03

047

2013

.00.

0268

020

13.1

0.02

523

2013

.30.

0274

620

13.3

0.04

092

2013

.30.

0167

120

13.3

0.02

011

20 –

24

2012

.10.

0252

020

12.0

0.01

224

2012

.10.

0258

620

12.0

0.02

331

2012

.10.

0222

820

12.2

0.02

246

2012

.20.

0309

920

12.1

0.01

443

2012

.20.

0177

6

25 -

29

2010

.70.

0262

020

10.5

0.01

334

2010

.60.

0268

520

10.6

0.02

448

2010

.70.

0234

320

10.5

0.02

294

2010

.60.

0302

820

10.4

0.01

550

2010

.50.

0192

8

30 –

34

2008

.80.

0295

220

08.5

0.01

564

2008

.70.

0303

920

08.7

0.02

733

2008

.70.

0265

220

08.4

0.02

581

2008

.70.

0330

820

08.3

0.01

837

2008

.50.

0230

2

35 –

39

2006

.50.

0334

420

06.1

0.01

833

2006

.30.

0346

320

06.4

0.03

038

2006

.40.

0302

820

06.1

0.02

879

2006

.40.

0358

520

05.9

0.02

174

2006

.10.

0279

9

40 –

44

2003

.70.

0369

220

03.3

0.02

146

2003

.50.

0398

620

03.7

0.03

333

2003

.70.

0336

320

03.5

0.03

229

2003

.90.

0389

520

03.2

0.02

515

2003

.40.

0330

1

45 -

49

2000

.30.

0412

219

99.9

0.02

409

1999

.90.

0449

220

00.6

0.03

551

2000

.40.

0372

520

00.5

0.03

448

2001

.00.

0401

319

99.9

0.02

763

2000

.30.

0376

0

Und

er-fi

ve m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

y ag

e 5)

: q(5

)

15 -

1920

13.1

0.09

653

2013

.00.

0888

920

13.1

0.09

761

2013

.00.

0938

120

13.1

0.09

249

2013

.30.

0991

520

13.3

0.11

025

2013

.30.

0892

720

13.3

0.08

896

20 –

24

2012

.10.

0865

020

12.0

0.08

132

2012

.10.

0866

820

12.0

0.08

491

2012

.10.

0846

920

12.2

0.08

627

2012

.20.

0891

620

12.1

0.08

181

2012

.20.

0829

1

25 -

29

2010

.70.

0889

420

10.5

0.08

609

2010

.60.

0890

720

10.6

0.08

793

2010

.70.

0877

320

10.5

0.08

747

2010

.60.

0876

020

10.4

0.08

551

2010

.50.

0868

3

30 –

34

2008

.80.

0969

320

08.5

0.09

568

2008

.70.

0974

320

08.7

0.09

512

2008

.70.

0958

320

08.4

0.09

485

2008

.70.

0936

120

08.3

0.09

440

2008

.50.

0960

9

35 –

39

2006

.50.

1060

820

06.1

0.10

630

2006

.30.

1071

320

06.4

0.10

259

2006

.40.

1052

720

06.1

0.10

252

2006

.40.

0995

420

05.9

0.10

437

2006

.10.

1074

6

40 –

44

2003

.70.

1139

720

03.3

0.11

798

2003

.50.

1186

320

03.7

0.10

965

2003

.70.

1134

320

03.5

0.11

136

2003

.90.

1061

320

03.2

0.11

475

2003

.40.

1179

7

45 -

49

2000

.30.

1233

919

99.9

0.12

730

1999

.90.

1293

920

00.6

0.11

472

2000

.40.

1219

820

00.5

0.11

683

2001

.00.

1086

019

99.9

0.12

210

2000

.30.

1268

6

Tabl

e B

2 (C

onti

nued

) In

dire

ct e

stim

ates

of

infa

nt, c

hild

and

und

er-fi

ve m

orta

lity

rate

s by

sex

acc

ordi

ng t

o di

ffer

ent

mor

talit

y m

odel

s, 2

014

Cen

sus

Cen

sus

Rep

ort

Vo

lum

e 4

-B –

Mo

rtal

ity

70

Ag

e g

roup

Uni

ted

Nat

ions

Mo

del

s (P

allo

ni-H

elig

man

Eq

uati

ons

)C

oal

e-D

emen

y M

od

el (

Trus

sell

Eq

uati

ons

)

Lati

n A

mer

ican

Chi

lean

Sout

h A

sian

Far

Eas

tG

ener

alW

est

No

rth

Eas

tSo

uth

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)R

efer

-en

ce

dat

eq

(x)

Ref

er-

ence

d

ate

q(x

)

Fem

ales

Infa

nt m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

etw

een

ages

0 a

nd 1

): q

(1)

15 -

1920

13.1

0.05

236

2013

.00.

0575

020

13.1

0.05

238

2013

.00.

0521

520

13.1

0.05

223

2013

.20.

0555

720

13.2

0.05

445

2013

.20.

0557

220

13.3

0.05

290

20 –

24

2012

.10.

0472

620

12.0

0.05

357

2012

.00.

0488

820

12.0

0.04

859

2012

.10.

0482

320

12.1

0.04

932

2012

.20.

0454

720

12.1

0.05

195

2012

.20.

0509

0

25 –

29

2010

.70.

0489

320

10.5

0.05

751

2010

.60.

0513

920

10.6

0.05

121

2010

.70.

0503

120

10.5

0.05

048

2010

.60.

0454

420

10.4

0.05

479

2010

.50.

0540

0

30 –

34

2008

.80.

0529

020

08.5

0.06

411

2008

.70.

0565

120

08.7

0.05

578

2008

.70.

0545

620

08.4

0.05

436

2008

.60.

0484

420

08.3

0.06

004

2008

.50.

0595

4

35 –

39

2006

.50.

0575

720

06.1

0.07

169

2006

.40.

0623

020

06.4

0.06

100

2006

.40.

0596

020

06.1

0.05

848

2006

.40.

0516

920

05.9

0.06

604

2006

.10.

0662

0

40 –

44

2003

.80.

0621

520

03.3

0.08

035

2003

.50.

0690

720

03.7

0.06

635

2003

.70.

0644

920

03.5

0.06

349

2003

.90.

0552

420

03.2

0.07

266

2003

.40.

0725

7

45 –

49

2000

.40.

0673

520

00.0

0.08

761

1999

.90.

0749

020

00.6

0.06

924

2000

.40.

0692

320

00.5

0.06

662

2001

.10.

0573

120

00.0

0.07

785

2000

.30.

0778

2

Chi

ld m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

etw

een

ages

1 a

nd 5

): q

(1,4

)

15 -

1920

13.1

0.02

460

2013

.00.

0101

220

13.1

0.01

908

2013

0.01

824

2013

.10.

0207

520

13.2

0.02

263

2013

.20.

0321

820

13.2

0.01

334

2013

.30.

0137

3

20 –

2420

12.1

0.02

058

2012

.00.

0089

520

12.0

0.01

703

2012

0.01

609

2012

.10.

0180

220

12.1

0.01

845

2012

.20.

0241

720

12.1

0.01

166

2012

.20.

0127

1

25 –

29

2010

.70.

0218

620

10.5

0.01

012

2010

.60.

0184

920

10.6

0.01

765

2010

.70.

0194

220

10.5

0.01

925

2010

.60.

0241

420

10.4

0.01

288

2010

.50.

0142

9

30 –

34

2008

.80.

0250

520

08.5

0.01

232

2008

.70.

0217

520

08.7

0.02

055

2008

.70.

0224

220

08.4

0.02

181

2008

.60.

0268

720

08.3

0.01

552

2008

.50.

0174

6

35 –

39

2006

.50.

0291

020

06.1

0.01

508

2006

.40.

0258

420

06.4

0.02

410

2006

.40.

0262

120

06.1

0.02

464

2006

.40.

0297

320

05.9

0.01

872

2006

.10.

0219

2

40 –

44

2003

.80.

0333

120

03.3

0.01

857

2003

.50.

0310

420

03.7

0.02

800

2003

.70.

0301

620

03.5

0.02

810

2003

.90.

0328

920

03.2

0.02

228

2003

.40.

0267

5

45 –

49

2000

.40.

0384

020

00.0

0.02

177

1999

.90.

0358

720

00.6

0.03

022

2000

.40.

0342

120

00.5

0.03

025

2001

.10.

0347

620

00.0

0.02

514

2000

.30.

0311

2

Und

er-fi

ve m

ort

alit

y ra

te (

pro

bab

ility

of

dyi

ng b

y ag

e 5)

: q(5

)

15 -

1920

13.1

0.07

567

2013

.00.

0670

420

13.1

0.07

046

2013

.00.

0694

420

13.1

0.07

190

2013

.20.

0769

520

13.2

0.08

488

2013

.20.

0683

220

13.3

0.06

590

20 –

24

2012

.10.

0668

720

12.0

0.06

204

2012

.00.

0650

820

12.0

0.06

390

2012

.10.

0653

720

12.1

0.06

686

2012

.20.

0685

420

12.1

0.06

300

2012

.20.

0629

6

25 –

29

2010

.70.

0697

320

10.5

0.06

705

2010

.60.

0689

220

10.6

0.06

796

2010

.70.

0687

520

10.5

0.06

875

2010

.60.

0684

920

10.4

0.06

697

2010

.50.

0675

1

30 –

34

2008

.80.

0766

220

08.5

0.07

563

2008

.70.

0770

320

08.7

0.07

519

2008

.70.

0757

520

08.4

0.07

499

2008

.60.

0740

020

08.3

0.07

463

2008

.50.

0759

7

35 –

39

2006

.50.

0850

020

06.1

0.08

569

2006

.40.

0865

320

06.4

0.08

363

2006

.40.

0842

520

06.1

0.08

168

2006

.40.

0798

820

05.9

0.08

352

2006

.10.

0866

7

40 –

44

2003

.80.

0933

920

03.3

0.09

743

2003

.50.

0979

620

03.7

0.09

248

2003

.70.

0927

120

03.5

0.08

980

2003

.90.

0863

120

03.2

0.09

332

2003

.40.

0973

8

45 –

49

2000

.40.

1031

720

00.0

0.10

747

1999

.90.

1080

820

00.6

0.09

736

2000

.40.

1010

720

00.5

0.09

486

2001

.10.

0900

820

00.0

0.10

103

2000

.30.

1065

1

Tabl

e B

2 (C

onti

nued

) In

dire

ct e

stim

ates

of

infa

nt, c

hild

and

und

er-fi

ve m

orta

lity

rate

s by

sex

acc

ordi

ng t

o di

ffer

ent

mor

talit

y m

odel

s, 2

014

Cen

sus


Appendix C. Infant, child and under-five mortality by Districts

The major problem of using a Brass-type method to estimate early-age mortality in subnational areas, is the size of the number of events, that is, the number of infant deaths. In principle, what this method does is to convert the proportion of children who have died among children ever born, reported by women (by age groups), into estimates of the probability of dying before attaining certain exact childhood ages. Later, improved versions of the Brass-type method were developed, in particular a method for locating the estimates for dates before the Census, which allowed for estimates as a time series, stretching back about 15 years before the Census.

Calculations of the early-age mortality indicators are computed by MORTPAK or any other similar software. The result is early-age mortality indicators at various dates prior to the Census (infant, child, and under-five). Normally data is categorized in seven age groups. This means there will be a series of seven estimates of under-five mortality, each dated somewhere from 1 to 15 years before the Census. However, the time series obtained cannot be taken at face value, but requires interpretation. The most recent point is highly erratic and it is usually ignored. The second point, corresponding to women aged 20-24, or the third point, corresponding to women aged 25-29, is considered to represent the most reliable recent estimate, and it is presented as the main result of the analysis, that is, the under-five mortality rate estimated by the Census. In addition, the mortality rate corresponding to women aged 20-24 or 25-29 is used as a pivot to estimate, via model life tables, the rates corresponding to the other dates (age groups).

The issue is that when this method is used to estimate under-five mortality in small areas, the number of deaths of children of mothers in the age groups 20-24 and 25-29 are usually very small and, therefore, likely to be affected by random factors. For example, annual fluctuations in mortality may have an effect in small population areas. Other elements that have an influence in the results of this method can also be affected by random factors due to the small size of the events (sex ratios, fertility, and migration). For this reason, this method is not recommended for estimating early-age mortality for small areas.

The strategy applied here considers a large number of cases, reducing the random components regarding the number of deaths and other factors that affect the results. This is done by considering the number of deaths of children of women aged 20-34 in the Districts (three age groups).

The method is based on identifying a relationship between the level of mortality in a District and the level in the State/Region where it is situated. The indicator of this mortality level is the proportion of children who have died among those children ever born to women aged 20-34. This proportion corresponds to the mortality difference between the District and its State/Region. The District’s mortality rates are obtained by applying (multiplying) this proportion to the infant, child or under-five mortality rate of the State/Region.

The basic principle of this method is that the expected level of mortality for each State/Region as a whole, is the result of the specific levels of each of its Districts in such a way that each one of them would contribute a portion to the mortality level of the State/Region.


The procedure to estimate mortality rates in the Districts is as follows:

(1) The procedure starts by calculating, for a given State/Region, the proportion of children who have died over children ever born to women aged 20-34.

(2) This proportion is applied to the number of children born to women aged 20-34 in each District of the State/Region under consideration. This operation gives the expected number of children who have died in each District, which is the number of deaths that would have occurred if the Districts have the same mortality level as the State/Region to which they belong.

(3) The ratio between the numbers of observed children who have died, and the number of expected children gives the factor k, which is an indicator of the difference in mortality between the State/Region and the District.

(4) Factor k is applied to (multiplied by) the infant mortality rate (1q0) estimated with the Brass-type method in the State/Region. This operation gives the 1q0 corresponding to the District.

(5) The same procedure, using the same k factor, is used to estimate child and under-five mortality.

By using a large number of non-surviving children in the estimations, the random factor is substantially reduced. In this case, the number of deaths of children corresponds to three age groups of women and it is large enough to avoid the influence of casual factors. This is the advantage of the method: to reduce fortuitous factors in the results.

Appendix C. Infant, child and under-five mortality by Districts


Table C Infant, child and under-five mortality by State/Region and District, 2014 Census

State/Region and District

Early-Age Mortality Rate


UNION

Both Sexes 61.8 10.0 71.8

Males 69.9 11.4 81.3

Females 53.6 8.4 62.0

KACHIN

Both Sexes 52.8 7.8 60.6

Males 58.2 8.6 66.8

Females 47.3 6.9 54.2

Myitkyina

Both Sexes 52.8 7.8 60.6

Males 58.1 8.5 66.6

Females 47.4 6.9 54.3

Mohnyin

Both Sexes 49.9 7.4 57.3

Males 55.3 8.2 63.5

Females 44.5 6.5 51.0

Bhamo

Both Sexes 51.8 7.7 59.5

Males 57.8 8.5 66.3

Females 45.8 6.7 52.5

Putao

Both Sexes 69.4 10.2 79.6

Males 74.0 10.9 84.9

Females 64.9 9.5 74.4

KAYAH

Both Sexes 60.1 9.6 69.7

Males 66.7 10.6 77.3

Females 53.2 8.3 61.5

Loikaw

Both Sexes 59.4 9.5 68.9

Males 66.2 10.5 76.7

Females 52.4 8.2 60.6

Bawlakhe

Both Sexes 63.8 10.2 74.0

Males 69.6 11.1 80.7

Females 57.6 9.1 66.7




KAYIN

Both Sexes 53.6 8.0 61.6

Males 59.7 8.9 68.6

Females 47.4 7.0 54.4

Hpa-an

Both Sexes 54.9 8.2 63.1

Males 61.3 9.1 70.4

Females 48.4 7.1 55.5

Pharpon

Both Sexes 58.7 8.8 67.5

Males 64.0 9.5 73.5

Females 53.3 7.8 61.1

Myawady

Both Sexes 41.1 6.2 47.3

Males 46.6 7.0 53.6

Females 35.4 5.1 40.5

Kawkareik

Both Sexes 57.4 8.5 65.9

Males 63.5 9.4 72.9

Females 51.3 7.5 58.8

CHIN

Both Sexes 75.5 14.1 89.6

Males 83.6 15.0 98.6

Females 67.6 12.6 80.2

Hakha

Both Sexes 27.5 5.1 32.6

Males 30.4 5.5 35.9

Females 24.6 4.5 29.1

Falam

Both Sexes 54.3 10.1 64.4

Males 60.2 10.8 71.0

Females 48.5 9.1 57.6

Mindat

Both Sexes 108.7 20.2 128.9

Males 119.6 21.4 141.0

Females 97.8 18.3 116.1





SAGAING

Both Sexes 60.0 9.6 69.6

Males 67.1 10.6 77.7

Females 52.8 8.3 61.1

Sagaing (District)

Both Sexes 41.6 6.7 48.3

Males 47.9 7.6 55.5

Females 35.3 5.5 40.8

Shwebo

Both Sexes 50.8 8.1 58.9

Males 57.8 9.2 67.0

Females 43.7 6.8 50.6

Monywa

Both Sexes 47.5 7.6 55.1

Males 52.5 8.3 60.8

Females 42.5 6.7 49.2

Katha

Both Sexes 70.7 11.3 82.0

Males 78.5 12.5 91.0

Females 62.8 9.8 72.6

Kalay

Both Sexes 61.2 9.8 71.0

Males 69.3 11.0 80.3

Females 53.0 8.3 61.3

Tamu

Both Sexes 52.9 8.5 61.4

Males 55.6 8.9 64.5

Females 50.2 7.9 58.1

Mawlaik

Both Sexes 78.4 12.6 91.0

Males 83.1 13.2 96.3

Females 73.8 11.5 85.3

Hkamti

Both Sexes 77.8 12.5 90.3

Males 85.5 13.6 99.1

Females 69.9 11.0 80.9

Yinmarpin

Both Sexes 66.7 10.6 77.3

Males 76.0 12.1 88.1

Females 57.2 9.0 66.2

Table C (Continued) Infant, child and under-five mortality by State/Region and District, 2014 Census




TANINTHARYI

Both Sexes 70.8 12.6 83.4

Males 77.1 13.2 90.3

Females 64.5 11.7 76.2

Dawei

Both Sexes 51.9 9.2 61.1

Males 55.4 9.6 65.0

Females 48.3 8.8 57.1

Myeik

Both Sexes 77.2 13.8 91.0

Males 83.4 14.3 97.7

Females 71.1 12.8 83.9

Kawthoung

Both Sexes 78.2 13.9 92.1

Males 88.6 15.2 103.8

Females 67.8 12.2 80.0

BAGO

Both Sexes 61.9 10.1 72.0

Males 72.6 12.1 84.7

Females 51.0 7.8 58.8

Bago (District)

Both Sexes 57.9 9.4 67.3

Males 68.4 11.3 79.7

Females 47.3 7.2 54.5

Taungoo

Both Sexes 68.5 11.2 79.7

Males 80.0 13.3 93.3

Females 56.8 8.7 65.5

Pyay

Both Sexes 56.7 9.3 66.0

Males 66.3 11.0 77.3

Females 47.0 7.2 54.2

Thayawady

Both Sexes 65.5 10.7 76.2

Males 76.6 12.8 89.4

Females 54.2 8.3 62.5






MAGWAY

Both Sexes 83.9 16.7 100.6

Males 96.5 18.7 115.2

Females 71.5 14.0 85.5

Magway (District)

Both Sexes 79.5 15.8 95.3

Males 91.0 17.7 108.7

Females 68.1 13.3 81.4

Minbu

Both Sexes 83.1 16.6 99.7

Males 95.3 18.5 113.8

Females 71.1 13.8 84.9

Thayet

Both Sexes 78.1 15.5 93.6

Males 89.8 17.4 107.2

Females 66.3 12.9 79.2

Pakokku

Both Sexes 95.8 19.1 114.9

Males 109.8 21.3 131.1

Females 81.9 15.9 97.8

Gangaw

Both Sexes 73.8 14.7 88.5

Males 87.9 17.1 105.0

Females 60.0 11.7 71.7




MANDALAY

Both Sexes 50.3 8.1 58.4

Males 59.1 8.8 67.9

Females 42.7 5.9 48.6

Mandalay (District)

Both Sexes 34.0 5.4 39.4

Males 40.4 6.0 46.4

Females 28.3 4.0 32.3

Pyin Oo Lwin

Both Sexes 49.6 7.9 57.5

Males 58.6 8.7 67.3

Females 41.8 5.8 47.6

Kyaukse

Both Sexes 49.6 7.9 57.5

Males 58.6 8.7 67.3

Females 41.8 5.8 47.6

Myingyan

Both Sexes 55.8 9.0 64.8

Males 64.8 9.6 74.4

Females 48.2 6.7 54.9

Nyaung U

Both Sexes 67.9 10.9 78.8

Males 79.7 11.8 91.5

Females 57.7 8.1 65.8

Yame`thin

Both Sexes 68.9 11.1 80.0

Males 81.2 12.1 93.3

Females 58.3 8.1 66.4

Meiktila

Both Sexes 60.9 9.8 70.7

Males 70.6 10.5 81.1

Females 52.6 7.3 59.9

MON

Both Sexes 41.9 5.4 47.3

Males 47.2 6.3 53.5

Females 36.2 4.8 41.0

Mawlamyine

Both Sexes 35.3 4.6 39.9

Males 40.6 5.3 45.9

Females 29.9 3.9 33.8

Thaton

Both Sexes 50.0 6.6 56.6

Males 55.6 7.3 62.9

Females 44.2 5.9 50.1





RAKHINE

Both Sexes 61.1 9.9 71.0

Males 67.3 10.8 78.1

Females 54.9 8.9 63.8

Sittwe

Both Sexes 48.0 7.8 55.8

Males 54.2 8.6 62.8

Females 42.0 6.7 48.7

Myauk U

Both Sexes 71.2 11.5 82.7

Males 78.4 12.6 91.0

Females 64.1 10.3 74.4

Maungtaw

Both Sexes 57.0 9.2 66.2

Males 58.6 9.4 68.0

Females 55.7 8.9 64.6

Kyaukpyu

Both Sexes 65.0 10.5 75.5

Males 71.3 11.4 82.7

Females 58.7 9.5 68.2

Thandwe

Both Sexes 56.0 9.1 65.1

Males 61.8 9.9 71.7

Females 50.3 8.1 58.4

YANGON

Both Sexes 44.9 6.1 51.0

Males 51.5 7.1 58.6

Females 38.2 5.2 43.4

North

Both Sexes 50.2 6.8 57.0

Males 57.8 7.9 65.7

Females 42.5 5.7 48.2

East

Both Sexes 32.8 4.4 37.2

Males 38.0 5.2 43.2

Females 27.4 3.7 31.1

South

Both Sexes 55.8 7.5 63.3

Males 63.5 8.7 72.2

Females 47.8 6.5 54.3

West

Both Sexes 25.7 3.5 29.2

Males 28.2 3.9 32.1

Females 23.2 3.1 26.3





SHAN

Both Sexes 55.5 8.5 64.0

Males 61.7 9.3 71.0

Females 49.4 7.4 56.8

Taunggyi

Both Sexes 61.4 9.4 70.8

Males 70.3 10.7 81.0

Females 52.5 7.9 60.4

Loilin

Both Sexes 60.9 9.3 70.2

Males 67.2 10.2 77.4

Females 54.7 8.2 62.9

Linkhe

Both Sexes 74.0 11.3 85.3

Males 81.3 12.4 93.7

Females 66.6 9.9 76.5

Lashio

Both Sexes 52.9 8.1 61.0

Males 58.6 8.9 67.5

Females 47.3 7.1 54.4

Muse

Both Sexes 39.5 6.1 45.6

Males 43.1 6.5 49.6

Females 36.0 5.4 41.4

Kyaukme

Both Sexes 69.2 10.6 79.8

Males 75.8 11.5 87.3

Females 62.7 9.3 72.0

Kunlon

Both Sexes 34.9 5.4 40.3

Males 39.4 6.0 45.4

Females 30.8 4.6 35.4

Laukine

Both Sexes 17.4 2.6 20.0

Males 20.0 3.0 23.0

Females 14.7 2.2 16.9

Hopan

Both Sexes 22.0 3.4 25.4

Males 23.2 3.5 26.7

Females 20.8 3.1 23.9

Makman

Both Sexes 59.5 9.1 68.6

Males 63.5 9.6 73.1

Females 55.5 8.3 63.8






Kengtung

Both Sexes 43.2 6.6 49.8

Males 48.4 7.4 55.8

Females 38.0 5.7 43.7

Minesat

Both Sexes 74.8 11.4 86.2

Males 80.4 12.2 92.6

Females 69.1 10.4 79.5

Tachilike

Both Sexes 42.6 6.5 49.1

Males 46.9 7.2 54.1

Females 38.2 5.7 43.9

Minephyat

Both Sexes 51.7 7.9 59.6

Males 58.0 8.8 66.8

Females 45.4 6.8 52.2

AYEYAWADY

Both Sexes 86.2 17.4 103.6

Males 98.3 19.3 117.6

Females 74.1 14.8 88.9

Pathein

Both Sexes 71.7 14.5 86.2

Males 82.3 16.1 98.4

Females 61.2 12.2 73.4

Phyapon

Both Sexes 96.1 19.4 115.5

Males 109.3 21.3 130.6

Females 82.9 16.6 99.5

Maubin

Both Sexes 76.7 15.4 92.1

Males 88.1 17.3 105.4

Females 65.3 13.0 78.3

Myaungmya

Both Sexes 83.7 17.0 100.7

Males 95.1 18.6 113.7

Females 72.5 14.6 87.1

Labutta

Both Sexes 123.5 24.9 148.4

Males 137.6 26.9 164.5

Females 109.3 21.9 131.2

Hinthada

Both Sexes 78.2 15.8 94.0

Males 91.2 17.8 109.0

Females 65.3 13.0 78.3




NAY PYI TAW

Both Sexes 55.4 8.4 63.8

Males 64.1 9.9 74.0

Females 46.6 6.8 53.4

Ottara

Both Sexes 53.8 8.2 62.0

Males 62.6 9.7 72.3

Females 44.9 6.5 51.4

Dekkhina

Both Sexes 56.8 8.7 65.5

Males 65.4 10.2 75.6

Females 48.2 7.0 55.2


Appendix D. Adjustment of household deaths during the 12 months prior to the Census

Table D1 Unadjusted deaths, death rates and probabilities of dying by sex, 2014 Census

Age group x n Population Deaths m(x,n) q(x,n) Indirect estimates of q(0,1) and q(1,4)*

Both sexes

Under 1 0 1 809,865 16,679 0.02059 0.02038 0.05878

1 – 4 1 4 3,602,987 6,752 0.00187 0.00747 0.00985

5 – 9 5 5 4,724,561 3,185 0.00067 0.00337 0.00668

10 -14 10 5 4,857,955 2,661 0.00055 0.00274 0.00554

15 – 19 15 5 4,260,063 3,805 0.00089 0.00446 0.00927

20 – 24 20 5 3,922,795 4,857 0.00124 0.00617 0.01331

25 – 29 25 5 3,835,001 6,665 0.00174 0.00865 0.01856

30 – 34 30 5 3,688,862 9,321 0.00253 0.01255 0.02644

35 – 39 35 5 3,408,280 11,520 0.00338 0.01676 0.03535

40 – 44 40 5 3,158,439 13,040 0.00413 0.02043 0.04347

45 – 49 45 5 2,846,351 14,255 0.00501 0.02473 0.05287

50 – 54 50 5 2,480,704 14,953 0.00603 0.02969 0.06375

55 – 59 55 5 1,992,677 16,225 0.00814 0.03990 0.08592

60 – 64 60 5 1,533,332 17,404 0.01135 0.05519 0.11844

65 – 69 65 5 1,032,828 16,902 0.01636 0.07861 0.17053

70 – 74 70 5 691,675 17,075 0.02469 0.11626 0.25257

75 – 79 75 5 535,331 20,117 0.03758 0.17176 0.36482

80 and over 80 inf. 548,293 37,551 0.06849 1 1

Males

Under 1 0 1 409,619 9,524 0.02325 0.02298 0.06671

1 – 4 1 4 1,822,552 3,603 0.00198 0.00788 0.01132

5 – 9 5 5 2,373,338 1,776 0.00075 0.00373 0.00373

10 – 14 10 5 2,395,227 1,473 0.00061 0.00307 0.00307

15 – 19 15 5 2,040,884 2,317 0.00114 0.00566 0.00566

20 – 24 20 5 1,809,125 3,094 0.00171 0.00851 0.00851

25 – 29 25 5 1,774,288 4,620 0.00260 0.01294 0.01294

30 – 34 30 5 1,732,410 6,832 0.00394 0.01953 0.01953

35 – 39 35 5 1,592,151 8,478 0.00532 0.02627 0.02627

40 – 44 40 5 1,457,800 9,331 0.00640 0.0315 0.0315

45 – 49 45 5 1,302,390 9,979 0.00766 0.03759 0.03759

50 – 54 50 5 1,125,573 9,802 0.00871 0.04261 0.04261

55 – 59 55 5 893,314 10,277 0.01150 0.05591 0.05591

60 – 64 60 5 680,750 10,452 0.01535 0.07393 0.07393

65 – 69 65 5 443,687 9,638 0.02172 0.10302 0.10302

70 – 74 70 5 286,187 9,149 0.03197 0.14801 0.14801

75 – 79 75 5 215,224 10,192 0.04736 0.21171 0.21171

80 and over 80 inf. 200,059 16,130 0.08063 1 1


Age group x n Population Deaths m(x,n) q(x,n) Indirect estimates of q(0,1) and q(1,4)*

Females

Under 1 0 1 400,246 7,155 0.01788 0.01772 0.05082

1 – 4 1 4 1,780,435 3,149 0.00177 0.00705 0.0081

5 – 9 5 5 2,351,223 1,409 0.00060 0.00299 0.00299

10 -14 10 5 2,462,728 1,188 0.00048 0.00241 0.00241

15 – 19 15 5 2,219,179 1,488 0.00067 0.00335 0.00335

20 – 24 20 5 2,113,670 1,763 0.00083 0.00416 0.00416

25 – 29 25 5 2,060,713 2,045 0.00099 0.00495 0.00495

30 – 34 30 5 1,956,452 2,489 0.00127 0.00634 0.00634

35 – 39 35 5 1,816,129 3,042 0.00167 0.00834 0.00834

40 – 44 40 5 1,700,639 3,709 0.00218 0.01085 0.01085

45 – 49 45 5 1,543,961 4,276 0.00277 0.01375 0.01375

50 – 54 50 5 1,355,131 5,151 0.00380 0.01883 0.01883

55 – 59 55 5 1,099,363 5,948 0.00541 0.02669 0.02669

60 – 64 60 5 852,582 6,952 0.00815 0.03996 0.03996

65 – 69 65 5 589,141 7,264 0.01233 0.05981 0.05981

70 – 74 70 5 405,488 7,926 0.01955 0.09318 0.09318

75 – 79 75 5 320,107 9,925 0.03101 0.14387 0.14387

80 and over 80 inf. 348,234 21,421 0.06151 1 1

* Estimated with data on children ever born and non-surviving children.

Note:










Inf. = infinity, which means the age interval is open after age 80.

Table D1 (Continued) Unadjusted deaths, death rates and probabilities of dying by sex, 2014 Census


Table D2 Percentage under-enumeration of adult mortality according to the Brass Growth Balance Equation Method, 2014 Census

AreaPercentage under-enumeration

Males Females

Union 29.7 37.5

Urban 31.8 36.1

Rural 29.2 38.2

Kachin 22.0 34.3

Kayah 25.4 32.7

Kayin 50.7 45.5

Chin 53.1 60.8

Sagaing 29.2 38.4

Tanintharyi 3.8 18.1

Bago 19.1 34.2

Magway 32.7 40.7

Mandalay 37.2 44.8

Mon 36.9 35.1

Rakhine 44.1 51.6

Yangon 33.4 39.6

Shan 44.9 45.9

Ayeyawady 13.0 21.8

Nay Pyi Taw 6.1 25.8

Table D3 Unadjusted Life Tables* based on deaths in the households within 12 months prior to the 2014 Census


Both sexes

0 0.02076 0.02038 100,000 2,038 98,168 0.97651 7,534,204 75.34 0.10

1 0.00188 0.00747 97,962 732 390,086 0.99402 7,436,036 75.91 1.59

5 0.00068 0.00337 97,230 328 485,332 0.99694 7,045,950 72.47 2.50

10 0.00055 0.00274 96,903 266 483,849 0.99655 6,560,619 67.70 2.50

15 0.00089 0.00446 96,637 431 482,180 0.99471 6,076,770 62.88 2.67

20 0.00124 0.00617 96,206 594 479,627 0.99268 5,594,590 58.15 2.64

25 0.00174 0.00865 95,612 827 476,115 0.98949 5,114,963 53.50 2.64

30 0.00252 0.01255 94,785 1,190 471,112 0.98534 4,638,848 48.94 2.63

35 0.00338 0.01676 93,596 1,569 464,207 0.98140 4,167,736 44.53 2.60

40 0.00413 0.02043 92,027 1,880 455,573 0.97748 3,703,529 40.24 2.57

45 0.00501 0.02473 90,147 2,229 445,314 0.97301 3,247,956 36.03 2.57

50 0.00602 0.02969 87,918 2,610 433,294 0.96567 2,802,641 31.88 2.59

55 0.00813 0.03990 85,307 3,404 418,418 0.95303 2,369,347 27.77 2.61

60 0.01134 0.05519 81,904 4,520 398,767 0.93409 1,950,929 23.82 2.62

65 0.01633 0.07861 77,383 6,083 372485 0.90416 1,552,162 20.06 2.63

70 0.02461 0.11626 71,300 8,289 336,786 0.85818 1,179,677 16.55 2.62

75 0.03745 0.17176 63,011 10,823 289,025 0.65710 842,890 13.38 2.59

80 0.09423 ... 52,188 52,188 553,866 ... 553,866 10.61 10.61

*These life tables were computed with the number of deaths in the household in the 12 months prior to the Census by age and sex and the population living in households. The data did not undergo any adjustment.



Males

0 0.02346 0.02298 100,000 2,298 97,951 0.97380 7,082,863 70.83 0.11

1 0.00198 0.00788 97,702 770 388,948 0.99355 6,984,911 71.49 1.58

5 0.00075 0.00373 96,932 362 483,757 0.99660 6,595,964 68.05 2.50

10 0.00061 0.00307 96,571 296 482,112 0.99587 6,112,207 63.29 2.50

15 0.00113 0.00566 96,274 545 480,123 0.99297 5,630,095 58.48 2.71

20 0.00171 0.00851 95,729 815 476,747 0.98943 5,149,972 53.80 2.67

25 0.00260 0.01294 94,915 1,228 471,709 0.98390 4,673,225 49.24 2.67

30 0.00394 0.01953 93,686 1,830 464,115 0.97706 4,201,516 44.85 2.64

35 0.00532 0.02627 91,857 2,413 453,467 0.97107 3,737,401 40.69 2.59

40 0.00640 0.03150 89,444 2,817 440,350 0.96547 3,283,934 36.72 2.56

45 0.00766 0.03759 86,626 3,256 425,146 0.96015 2,843,584 32.83 2.55

50 0.00870 0.04261 83,370 3,552 408,204 0.95136 2,418,437 29.01 2.57

55 0.01149 0.05591 79,817 4,463 388,350 0.93577 2,010,233 25.19 2.59

60 0.01533 0.07393 75,355 5,571 363,405 0.91280 1,621,883 21.52 2.60

65 0.02167 0.10302 69,784 7,189 331,718 0.87635 1,258,478 18.03 2.61

70 0.03187 0.14801 62,595 9,265 290,700 0.82265 926760 14.81 2.60

75 0.04721 0.21171 53,330 11,291 239,145 0.62402 636,061 11.93 2.56

80 0.10592 ... 42,040 42,040 396,916 ... 396,916 9.44 9.44

Females

0 0.01801 0.01772 100,000 1,772 98,411 0.97918 7,956,994 79.57 0.10

1 0.00177 0.00705 98,228 693 391,177 0.99461 7,858,583 80.00 1.50

5 0.00060 0.00299 97,535 292 486,948 0.99730 7,467,406 76.56 2.50

10 0.00048 0.00241 97,244 234 485,633 0.99720 6,980,458 71.78 2.50

15 0.00067 0.00335 97,010 325 484,272 0.99624 6,494,824 66.95 2.61

20 0.00083 0.00416 96,685 402 482,449 0.99546 6,010,553 62.17 2.58

25 0.00099 0.00495 96,282 477 480,261 0.99441 5,528,103 57.42 2.59

30 0.00127 0.00634 95,806 607 477,575 0.99271 5,047,842 52.69 2.61

35 0.00167 0.00834 95,198 794 474,093 0.99045 4,570,268 48.01 2.61

40 0.00218 0.01085 94,404 1,024 469,564 0.98779 4,096,175 43.39 2.60

45 0.00277 0.01375 93,380 1,284 463,831 0.98392 3,626,611 38.84 2.61

50 0.00380 0.01883 92,096 1,734 456,373 0.97757 3,162,779 34.34 2.63

55 0.00541 0.02669 90,362 2,412 446,136 0.96721 2,706,406 29.95 2.65

60 0.00814 0.03996 87,950 3,514 431,507 0.95095 2,260,270 25.70 2.65

65 0.01231 0.05981 84,436 5,050 410,342 0.92497 1,828,763 21.66 2.66

70 0.01949 0.09318 79,386 7,397 379,552 0.88359 1,418,421 17.87 2.65

75 0.03088 0.14387 71,988 10,357 335,368 0.67718 1,038,869 14.43 2.63

80 0.08761 ... 61,631 61,631 703,500 ... 703,500 11.41 11.41

*These life tables were computed with the number of deaths in the household in the 12 months prior to the Census

by age and sex and the population living in households. The data did not undergo any adjustment.

Table D3 (Continued) Unadjusted Life Tables* based on deaths in the households within 12 months prior to the 2014 Census


Table D4 Unadjusted Life Tables with q(0,1) and q(1,4) estimated indirectly using data on children ever born and non-surviving children*, 2014 Census


Both sexes

0 0.07030 0.06671 100,000 6,671 94,892 0.93103 6,744,407 67.44 0.23

1 0.00285 0.01132 93,329 1,056 370,624 0.98923 6,649,515 71.25 1.45

5 0.00075 0.00373 92,273 344 460,502 0.99660 6,278,891 68.05 2.50

10 0.00061 0.00307 91,928 282 458,936 0.99587 5,818,389 63.29 2.50

15 0.00113 0.00566 91,646 519 457,043 0.99297 5,359,453 58.48 2.71

20 0.00171 0.00851 91,127 775 453,830 0.98943 4,902,410 53.80 2.67

25 0.00260 0.01294 90,352 1,169 449,034 0.98390 4,448,580 49.24 2.67

30 0.00394 0.01953 89,183 1,742 441,804 0.97706 3,999,546 44.85 2.64

35 0.00532 0.02627 87,441 2,297 431,669 0.97107 3,557,742 40.69 2.59

40 0.00640 0.03150 85,144 2,682 419,182 0.96547 3,126,073 36.72 2.56

45 0.00766 0.03759 82,462 3,100 404,709 0.96015 2,706,891 32.83 2.55

50 0.00870 0.04261 79,362 3,382 388,581 0.95136 2,302,181 29.01 2.57

55 0.01149 0.05591 75,981 4,248 369,682 0.93577 1,913,600 25.19 2.59

60 0.01533 0.07393 71,732 5,303 345,936 0.91280 1,543,918 21.52 2.60

65 0.02167 0.10302 66,429 6,844 315,772 0.87635 1,197,982 18.03 2.61

70 0.03187 0.14801 59,586 8,819 276,726 0.82265 882,210 14.81 2.60

75 0.04721 0.21171 50,766 10,748 227,649 0.62402 605,485 11.93 2.56

80 0.10592 ... 40,019 40,019 377,836 ... 377,836 9.44 9.44

Males

0 0.06164 0.05878 100,000 5,878 95,365 0.93903 6,151,277 61.51 0.21

1 0.00248 0.00985 94,122 927 374,148 0.98915 6,055,912 64.34 1.48

5 0.00134 0.00668 93,195 623 464,418 0.99389 5,681,764 60.97 2.50

10 0.00111 0.00554 92,572 513 461,580 0.99293 5,217,346 56.36 2.50

15 0.00186 0.00927 92,060 853 458,317 0.98876 4,755,766 51.66 2.68

20 0.00268 0.01331 91,206 1,214 453,166 0.98422 4,297,449 47.12 2.64

25 0.00374 0.01856 89,992 1,670 446,013 0.97769 3,844,283 42.72 2.64

30 0.00536 0.02644 88,322 2,335 436,063 0.96915 3,398,270 38.48 2.62

35 0.00719 0.03535 85,987 3,040 422,609 0.96061 2,962,207 34.45 2.59

40 0.00888 0.04347 82,947 3,606 405,963 0.95198 2,539,598 30.62 2.57

45 0.01085 0.05287 79,341 4,195 386,468 0.94219 2,133,635 26.89 2.56

50 0.01316 0.06375 75,147 4,791 364,126 0.92621 1,747,167 23.25 2.58

55 0.01792 0.08592 70,356 6,045 337,256 0.89923 1,383,041 19.66 2.60

60 0.02512 0.11844 64,311 7,617 303,271 0.85806 1,045,785 16.26 2.60

65 0.03715 0.17053 56,694 9,668 260,224 0.79232 742,514 13.10 2.60

70 0.05761 0.25257 47,026 11,877 206,180 0.69669 482,291 10.26 2.56

75 0.08927 0.36482 35,149 12,823 143,643 0.47976 276,111 7.86 2.50

80 0.16854 ... 22,326 22,326 132,468 ... 132,468 5.93 5.93

* These life tables were computed with the number of deaths in the household in the 12 months prior to the Census

by age and sex and the population living in households, except for the first two age groups (0-1 and 1-4 years). For

these age groups mortality was estimated indirectly using data on children ever born and non-surviving children.

Other data did not undergo any adjustment.



Females

0 0.05297 0.05082 100,000 5,082 95947 0.94730 7,681,797 76.82 0.20

1 0.00204 0.00810 94,918 769 377705 0.99238 7,585,850 79.92 1.44

5 0.00060 0.00299 94,149 282 470042 0.99730 7,208,146 76.56 2.50

10 0.00048 0.00241 93,868 226 468773 0.99720 6,738,104 71.78 2.50

15 0.00067 0.00335 93,641 314 467458 0.99624 6,269,331 66.95 2.61

20 0.00083 0.00416 93,328 388 465699 0.99546 5,801,873 62.17 2.58

25 0.00099 0.00495 92,939 460 463587 0.99441 5,336,174 57.42 2.59

30 0.00127 0.00634 92,479 586 460994 0.99271 4,872,587 52.69 2.61

35 0.00167 0.00834 91,893 766 457633 0.99045 4,411,593 48.01 2.61

40 0.00218 0.01085 91,127 989 453261 0.98779 3,953,960 43.39 2.60

45 0.00277 0.01375 90,138 1,239 447728 0.98392 3,500,699 38.84 2.61

50 0.00380 0.01883 88,899 1,674 440528 0.97757 3,052,971 34.34 2.63

55 0.00541 0.02669 87,225 2,328 430647 0.96721 2,612,443 29.95 2.65

60 0.00814 0.03996 84,897 3,392 416526 0.95095 2,181,796 25.70 2.65

65 0.01231 0.05981 81,504 4,875 396095 0.92497 1,765,270 21.66 2.66

70 0.01949 0.09318 76,629 7,140 366375 0.88359 1,369,175 17.87 2.65

75 0.03088 0.14387 69,489 9,997 323725 0.67718 1,002,800 14.43 2.63

80 0.08761 ... 59,492 59,492 679076 ... 679,076 11.41 11.41

* These life tables were computed with the number of deaths in the household in the 12 months prior to the Census

by age and sex and the population living in households, except for the first two age groups (0-1 and 1-4 years). For

these age groups mortality was estimated indirectly using data on children ever born and non-surviving children.

Other data did not undergo any adjustment.

Table D4 (Continued) Unadjusted Life Tables with q(0,1) and q(1,4) estimated indirectly using data on children ever born and non-surviving children*, 2014 Census


Table D5 Adjusted Life Tables*, 2014 Census


Union

Both sexes Union

0 0.06164 0.05878 100,000 5,878 95,365 0.93903 6,469,721 64.70 0.21

1 0.00248 0.00985 94,122 927 374,148 0.99024 6,374,356 67.72 1.48

5 0.00090 0.00448 93,195 418 464,931 0.99594 6,000,208 64.38 2.50

10 0.00073 0.00364 92,777 338 463,043 0.99542 5,535,278 59.66 2.50

15 0.00119 0.00591 92,440 546 460,923 0.99299 5,072,235 54.87 2.67

20 0.00164 0.00817 91,893 751 457,691 0.99032 4,611,312 50.18 2.63

25 0.00229 0.01141 91,143 1,040 453,261 0.98616 4,153,621 45.57 2.64

30 0.00333 0.01651 90,103 1,488 446,989 0.98074 3,700,360 41.07 2.63

35 0.00445 0.02202 88,615 1,951 438,379 0.97555 3,253,370 36.71 2.59

40 0.00545 0.02687 86,664 2,329 427,663 0.97053 2,814,991 32.48 2.57

45 0.00661 0.03253 84,335 2,743 415,061 0.96244 2,387,329 28.31 2.59

50 0.00892 0.04368 81,592 3,564 399,470 0.94822 1,972,267 24.17 2.62

55 0.01275 0.06190 78,028 4,830 378,786 0.92151 1,572,797 20.16 2.65

60 0.02077 0.09903 73,198 7,249 349,056 0.87370 1,194,011 16.31 2.66

65 0.03447 0.15942 65,949 10,514 304,970 0.79700 844,956 12.81 2.64

70 0.05839 0.25601 55,435 14,192 243,061 0.68211 539,985 9.74 2.60

75 0.09805 0.39416 41,243 16,257 165,795 0.44162 296,924 7.20 2.51

80 0.19055 ... 24,987 24,987 131,129 ... 131,129 5.25 5.25

Males Union

0 0.07030 0.06671 100,000 6,671 94,892 0.93103 6,017,116 60.17 0.23

1 0.00285 0.01132 93,329 1,056 370,624 0.98868 5,922,224 63.46 1.45

5 0.00097 0.00484 92,273 447 460,246 0.99559 5,551,600 60.17 2.50

10 0.00080 0.00398 91,826 365 458,216 0.99465 5,091,354 55.45 2.50

15 0.00147 0.00734 91,460 671 455,765 0.99088 4,633,138 50.66 2.71

20 0.00222 0.01103 90,789 1,001 451,610 0.98632 4,177,373 46.01 2.67

25 0.00337 0.01674 89,788 1,503 445,432 0.97918 3,725,763 41.50 2.67

30 0.00511 0.02525 88,285 2,229 436,159 0.97035 3,280,331 37.16 2.64

35 0.00690 0.03395 86,055 2,922 423,226 0.96264 2,844,172 33.05 2.59

40 0.00830 0.04066 83,134 3,380 407,417 0.95572 2,420,946 29.12 2.56

45 0.00993 0.04848 79,754 3,866 389,378 0.94523 2,013,529 25.25 2.57

50 0.01290 0.06255 75,887 4,747 368,053 0.92613 1,624,151 21.40 2.60

55 0.01842 0.08825 71,140 6,278 340,863 0.88884 1,256,099 17.66 2.64

60 0.02984 0.13938 64,862 9,041 302,974 0.82515 915,235 14.11 2.64

65 0.04857 0.21750 55,822 12,141 250,000 0.73073 612,261 10.97 2.60

70 0.07929 0.33161 43,681 14,485 182,681 0.60366 362,261 8.29 2.53

75 0.12627 0.47693 29,196 13,924 110,276 0.38592 179,580 6.15 2.44

80 0.22035 ... 15,271 15,271 69,304 ... 69,304 4.54 4.54

* These life table correspond to those presented in Table 3.2 in Chapter 3. They were computed with the number of

deaths in the household during the 12 months prior to the Census and the population living in households adjusted

using the Brass Growth Balance Equation method. For the first two age groups (0-1 and 1-4 years) mortality was

estimated indirectly using the number of children ever born and non-surviving children.



Females Union

0 0.05297 0.05082 100,000 5,082 95,947 0.94730 6,933,432 69.33 0.20

1 0.00204 0.00810 94,918 769 377,705 0.99182 6,837,485 72.04 1.44

5 0.00082 0.00411 94,149 387 469,778 0.99629 6,459,780 68.61 2.50

10 0.00066 0.00331 93,762 310 468,035 0.99615 5,990,002 63.89 2.50

15 0.00092 0.00460 93,452 430 466,233 0.99483 5,521,967 59.09 2.61

20 0.00115 0.00572 93,022 532 463,822 0.99377 5,055,734 54.35 2.58

25 0.00136 0.00680 92,490 629 460,931 0.99232 4,591,912 49.65 2.59

30 0.00175 0.00871 91,861 800 457,389 0.98999 4,130,982 44.97 2.61

35 0.00230 0.01145 91,061 1,043 452,810 0.98689 3,673,593 40.34 2.61

40 0.00300 0.01488 90,018 1,339 446,874 0.98332 3,220,783 35.78 2.60

45 0.00381 0.01886 88,679 1,672 439,418 0.97710 2,773,909 31.28 2.62

50 0.00562 0.02773 87,006 2,413 429,353 0.96676 2,334,491 26.83 2.65

55 0.00814 0.03994 84,594 3,379 415,082 0.94875 1,905,137 22.52 2.67

60 0.01352 0.06554 81,215 5,323 393,810 0.91332 1,490,056 18.35 2.70

65 0.02387 0.11311 75,892 8,584 359,673 0.84878 1,096,245 14.44 2.69

70 0.04374 0.19839 67,308 13,353 305,285 0.74143 736,572 10.94 2.66

75 0.07944 0.33327 53,955 17,981 226,349 0.47518 431,286 7.99 2.59

80 0.17553 ... 35,973 35,973 204,938 ... 204,938 5.70 5.70

* These life table correspond to those presented in Table 3.2 in Chapter 3. They were computed with the number of

deaths in the household during the 12 months prior to the Census and the population living in households adjusted

using the Brass Growth Balance Equation method. For the first two age groups (0-1 and 1-4 years) mortality was

estimated indirectly using the number of children ever born and non-surviving children.

Note:










Table D5 (Continued) Adjusted Life Tables*, 2014 Census


Appendix E. Life tables for urban and rural place of residence and for State/RegionTable E Life Tables for urban and rural place of residence and for State/Region, 2014 Census

Urban areasAge m(x,n) q(x,n) l(x) d(x,n) L(x,n) S(x,n) T(x) e(x) a(x,n)

Union

Both sexes Union

0 0.03776 0.03658 100,000 3,658 96,882 0.96233 6,524,300 65.24 0.15

1 0.00115 0.00459 96,342 442 384,281 0.99486 6,427,418 66.71 1.54

5 0.00068 0.00338 95,900 324 478,689 0.99682 6,043,137 63.02 2.5

10 0.00059 0.00297 95,576 284 477,169 0.99658 5,564,448 58.22 2.5

15 0.00084 0.00418 95,292 398 475,536 0.99450 5,087,279 53.39 2.68

20 0.00144 0.00716 94,893 679 472,921 0.99044 4,611,744 48.60 2.72

25 0.00249 0.01237 94,214 1,165 468,402 0.98393 4,138,823 43.93 2.71

30 0.00404 0.02003 93,049 1,864 460,874 0.97640 3,670,421 39.45 2.66

35 0.00547 0.02700 91,185 2,462 449,996 0.97008 3,209,547 35.20 2.59

40 0.00665 0.03271 88,723 2,902 436,533 0.96468 2,759,551 31.10 2.56

45 0.00780 0.03826 85,821 3,284 421,114 0.95729 2,323,018 27.07 2.57

50 0.00991 0.04839 82,537 3,994 403,129 0.94233 1,901,904 23.04 2.61

55 0.01440 0.06963 78,543 5,469 379,879 0.91108 1,498,775 19.08 2.65

60 0.02382 0.11282 73,074 8,244 346,099 0.85598 1,118,895 15.31 2.66

65 0.03981 0.18191 64,830 11,793 296,252 0.76905 772,797 11.92 2.63

70 0.06760 0.29039 53,037 15,401 227,832 0.64419 476,545 8.99 2.57

75 0.11206 0.43700 37,635 16,447 146,767 0.40989 248,713 6.61 2.48

80 0.20784 ... 21,189 21,189 101,945 ... 101,945 4.81 4.81

Males Union

0 0.04338 0.04186 100,000 4,186 96,496 0.95695 5,970,215 59.7 0.16

1 0.00135 0.00538 95,814 515 381,981 0.99385 5,873,719 61.3 1.53

5 0.00081 0.00402 95,299 383 475,535 0.99628 5,491,738 57.63 2.50

10 0.00068 0.00341 94,915 324 473,768 0.99585 5,016,203 52.85 2.50

15 0.00108 0.00540 94,592 511 471,800 0.99241 4,542,435 48.02 2.73

20 0.00209 0.01042 94,081 980 468,220 0.98511 4,070,636 43.27 2.77

25 0.00407 0.02018 93,101 1,879 461,248 0.97350 3,602,415 38.69 2.73

30 0.00673 0.03314 91,222 3,023 449,025 0.96071 3,141,168 34.43 2.66

35 0.00924 0.04519 88,199 3,986 431,385 0.94968 2,692,143 30.52 2.59

40 0.01132 0.05508 84,213 4,638 409,676 0.94155 2,260,758 26.85 2.54

45 0.01282 0.06213 79,575 4,944 385,729 0.93174 1,851,082 23.26 2.54

50 0.01587 0.07642 74,631 5,703 359,401 0.90940 1,465,353 19.63 2.59

55 0.02299 0.10901 68,927 7,514 326,839 0.86264 1,105,952 16.05 2.63

60 0.03751 0.17220 61,414 10,575 281,944 0.78566 779,113 12.69 2.62

65 0.06075 0.26469 50,838 13,456 221,512 0.67822 497,169 9.78 2.57

70 0.09705 0.39005 37,382 14,581 150,234 0.54562 275,657 7.37 2.48

75 0.14896 0.53553 22,801 12,211 81,971 0.34645 125,424 5.5 2.38

80 0.24372 ... 10,590 10,590 43,453 ... 43,453 4.1 4.10



Females Union

0 0.03191 0.03106 100,000 3,106 97,339 0.96790 7,096,103 70.96 0.14

1 0.00099 0.00394 96,894 382 386,611 0.99577 6,998,764 72.23 1.47

5 0.00055 0.00273 96,512 263 481,902 0.99737 6,612,153 68.51 2.50

10 0.00051 0.00253 96,249 244 480,635 0.99729 6,130,250 63.69 2.50

15 0.00061 0.00303 96,005 291 479,332 0.99635 5,649,615 58.85 2.61

20 0.00087 0.00435 95,714 416 477,584 0.99505 5,170,284 54.02 2.63

25 0.00113 0.00563 95,298 537 475,220 0.99312 4,692,700 49.24 2.63

30 0.00167 0.00831 94,761 787 471,948 0.99028 4,217,480 44.51 2.64

35 0.00224 0.01114 93,974 1,047 467,362 0.98751 3,745,532 39.86 2.60

40 0.00281 0.01398 92,927 1,299 461,527 0.98368 3,278,170 35.28 2.61

45 0.00385 0.01907 91,628 1,747 453,992 0.97752 2,816,643 30.74 2.63

50 0.00537 0.02649 89,881 2,381 443,788 0.96785 2,362,650 26.29 2.64

55 0.00801 0.03930 87,500 3,439 429,518 0.94869 1,918,863 21.93 2.68

60 0.01375 0.06663 84,061 5,601 407,480 0.91016 1,489,344 17.72 2.71

65 0.02520 0.11914 78,460 9,348 370,873 0.83789 1,081,864 13.79 2.71

70 0.04795 0.21560 69,112 14,901 310,751 0.71704 710,991 10.29 2.66

75 0.08937 0.36732 54,212 19,913 222,819 0.44329 400,240 7.38 2.58

80 0.19332 ... 34,299 34,299 177,421 ... 177,421 5.17 5.17

Rural AreasUnion

Both sexes Union

0 0.06796 0.06457 100,000 6,457 95,016 0.93287 6,472,876 64.73 0.23

1 0.00291 0.01157 93,543 1,082 371,421 0.98875 6,377,860 68.18 1.46

5 0.00097 0.00482 92,461 446 461,189 0.99565 6,006,439 64.96 2.50

10 0.00078 0.00388 92,015 357 459,183 0.99495 5,545,250 60.26 2.50

15 0.00134 0.00666 91,658 610 456,865 0.99230 5,086,067 55.49 2.66

20 0.00174 0.00866 91,048 788 453,347 0.99024 4,629,202 50.84 2.60

25 0.00221 0.01101 90,259 994 448,921 0.98713 4,175,855 46.27 2.61

30 0.00302 0.01498 89,265 1,337 443,142 0.98259 3,726,935 41.75 2.62

35 0.00402 0.01991 87,928 1,751 435,428 0.97791 3,283,793 37.35 2.59

40 0.00492 0.02431 86,178 2,095 425,810 0.97311 2,848,365 33.05 2.58

45 0.00609 0.03001 84,083 2,523 414,361 0.96466 2,422,555 28.81 2.60

50 0.00851 0.04172 81,559 3,403 399,716 0.95060 2,008,195 24.62 2.63

55 0.01210 0.05883 78,157 4,598 379,971 0.92557 1,608,479 20.58 2.65

60 0.01961 0.09376 73,559 6,897 351,690 0.88036 1,228,507 16.70 2.67

65 0.03254 0.15112 66,662 10,074 309,613 0.80715 876,817 13.15 2.65

70 0.05519 0.24371 56,588 13,791 249,905 0.69554 567,205 10.02 2.60

75 0.09337 0.37922 42,797 16,229 173,818 0.45220 317,300 7.41 2.53

80 0.18516 ... 26,567 26,567 143,482 ... 143,482 5.4 5.4

Appendix E. Life Tables for urban and rural place of residence and for State/Region, 2014 Census



Males Union

0 0.07763 0.07338 100,000 7,338 94,522 0.92405 6,072,253 60.72 0.25

1 0.00333 0.01321 92,662 1,224 367,504 0.98701 5,977,731 64.51 1.43

5 0.00102 0.00509 91,438 465 456,026 0.99536 5,610,227 61.36 2.50

10 0.00084 0.00418 90,973 380 453,912 0.99417 5,154,201 56.66 2.50

15 0.00164 0.00816 90,592 739 451,265 0.99021 4,700,289 51.88 2.71

20 0.00228 0.01133 89,853 1,018 446,849 0.98683 4,249,024 47.29 2.63

25 0.00307 0.01526 88,835 1,356 440,963 0.98166 3,802,175 42.80 2.63

30 0.00440 0.02179 87,479 1,906 432,875 0.97448 3,361,212 38.42 2.63

35 0.00593 0.02922 85,573 2,500 421,827 0.96814 2,928,338 34.22 2.58

40 0.00702 0.03450 83,073 2,866 408,388 0.96173 2,506,511 30.17 2.57

45 0.00874 0.04278 80,207 3,431 392,760 0.95076 2,098,122 26.16 2.59

50 0.01172 0.05699 76,775 4,375 373,420 0.93276 1,705,363 22.21 2.61

55 0.01664 0.08007 72,400 5,797 348,310 0.89917 1,331,943 18.40 2.64

60 0.02689 0.12643 66,603 8,421 313,191 0.84091 983,634 14.77 2.65

65 0.04385 0.19848 58,182 11,548 263,364 0.75242 670,443 11.52 2.61

70 0.07223 0.30692 46,634 14,313 198,160 0.62884 407,078 8.73 2.55

75 0.11692 0.45077 32,321 14,569 124,611 0.40354 208,919 6.46 2.46

80 0.21056 ... 17,752 17,752 84,308 ... 84,308 4.75 4.75

Females Union

0 0.05843 0.05588 100,000 5,588 95,628 0.94190 6,879,063 68.79 0.22

1 0.00241 0.00959 94,412 905 375,324 0.99048 6,783,434 71.85 1.43

5 0.00091 0.00455 93,507 425 466,469 0.99593 6,408,111 68.53 2.50

10 0.00072 0.00359 93,081 334 464,570 0.99570 5,941,641 63.83 2.50

15 0.00106 0.00527 92,747 489 462,570 0.99414 5,477,071 59.05 2.62

20 0.00128 0.00637 92,258 588 459,861 0.99316 5,014,501 54.35 2.57

25 0.00147 0.00733 91,671 672 456,717 0.99196 4,554,640 49.68 2.57

30 0.00179 0.00889 90,999 809 453,045 0.98985 4,097,922 45.03 2.59

35 0.00233 0.01160 90,190 1,046 448,447 0.98659 3,644,877 40.41 2.61

40 0.00308 0.01531 89,143 1,365 442,434 0.98314 3,196,431 35.86 2.59

45 0.00379 0.01877 87,779 1,648 434,975 0.97687 2,753,997 31.37 2.62

50 0.00575 0.02836 86,131 2,443 424,916 0.96611 2,319,022 26.92 2.65

55 0.00826 0.04052 83,688 3,391 410,513 0.94827 1,894,106 22.63 2.66

60 0.01359 0.06587 80,297 5,289 389,277 0.91337 1,483,593 18.48 2.69

65 0.02373 0.11249 75,008 8,438 355,554 0.85045 1,094,316 14.59 2.69

70 0.0430 0.19533 66,570 13,003 302,381 0.74631 738,762 11.10 2.66

75 0.07737 0.32596 53,567 17,461 225,669 0.48286 436,381 8.15 2.59

80 0.17135 ... 36,106 36,106 210,712 ... 210,712 5.84 5.84




Kachin

Both sexes Kachin

0 0.04986 0.04790 100,000 4,790 96,073 0.95053 6,423,411 64.23 0.18

1 0.00174 0.00694 95,210 661 379,194 0.99219 6,327,338 66.46 1.51

5 0.00101 0.00504 94,549 477 471,555 0.99536 5,948,144 62.91 2.50

10 0.00085 0.00424 94,073 399 469,366 0.99452 5,476,589 58.22 2.50

15 0.00149 0.00741 93,674 694 466,796 0.98990 5,007,223 53.45 2.73

20 0.00264 0.01311 92,980 1,219 462,079 0.98403 4,540,427 48.83 2.69

25 0.00375 0.01858 91,761 1,705 454,699 0.98008 4,078,348 44.45 2.59

30 0.00427 0.02114 90,056 1,904 445,643 0.97625 3,623,648 40.24 2.56

35 0.00534 0.02636 88,152 2,324 435,060 0.97294 3,178,005 36.05 2.55

40 0.00564 0.02781 85,828 2,387 423,287 0.96916 2,742,945 31.96 2.55

45 0.00709 0.03484 83,441 2,907 410,235 0.95906 2,319,658 27.80 2.60

50 0.00986 0.04816 80,534 3,879 393,439 0.94328 1,909,423 23.71 2.62

55 0.01388 0.06719 76,656 5,151 371,125 0.91570 1,515,984 19.78 2.64

60 0.02220 0.10549 71,505 7,543 339,838 0.86628 1,144,859 16.01 2.65

65 0.03646 0.16781 63,962 10,734 294,395 0.78924 805,021 12.59 2.63

70 0.06026 0.26302 53,229 14,000 232,348 0.67453 510,626 9.59 2.59

75 0.10068 0.40222 39,229 15,778 156,726 0.43680 278,277 7.09 2.50

80 0.19292 ... 23,450 23,450 121,552 ... 121,552 5.18 5.18

Males Kachin

0 0.05559 0.05321 100,000 5,321 95,719 0.94515 5,936,283 59.36 0.2

1 0.00197 0.00784 94,679 742 376,855 0.99110 5,840,564 61.69 1.49

5 0.00112 0.00560 93,937 526 468,368 0.99488 5,463,709 58.16 2.50

10 0.00093 0.00464 93,411 433 465,970 0.99325 4,995,340 53.48 2.50

15 0.00204 0.01013 92,977 942 462,826 0.98471 4,529,371 48.71 2.81

20 0.00426 0.02109 92,035 1,941 455,748 0.97416 4,066,544 44.18 2.72

25 0.00606 0.02984 90,094 2,688 443,973 0.96838 3,610,796 40.08 2.58

30 0.00673 0.03311 87,406 2,894 429,935 0.96341 3,166,823 36.23 2.55

35 0.00818 0.04008 84,512 3,387 414,205 0.95890 2,736,888 32.38 2.53

40 0.00857 0.04198 81,125 3,406 397,182 0.95566 2,322,683 28.63 2.52

45 0.00986 0.04817 77,719 3,744 379,572 0.94183 1,925,502 24.78 2.59

50 0.01456 0.07037 73,975 5,206 357,493 0.91737 1,545,930 20.90 2.62

55 0.02039 0.09726 68,770 6,689 327,952 0.87909 1,188,437 17.28 2.62

60 0.03227 0.14987 62,081 9,304 288,301 0.81476 860,485 13.86 2.62

65 0.05111 0.22749 52,777 12,006 234,896 0.72249 572,184 10.84 2.59

70 0.08110 0.33756 40,771 13,763 169,709 0.60083 337,288 8.27 2.52

75 0.12599 0.47567 27,008 12,847 101,966 0.39153 167,579 6.20 2.43

80 0.21583 ... 14,161 14,161 65,613 ... 65,613 4.63 4.63




Females Kachin

0 0.04400 0.04246 100,000 4,246 96,507 0.95616 6,930,798 69.31 0.18

1 0.00149 0.00592 95,754 567 381,573 0.99329 6,834,291 71.37 1.45

5 0.00089 0.00446 95,187 425 474,874 0.99585 6,452,718 67.79 2.50

10 0.00077 0.00383 94,763 363 472,906 0.99578 5,977,843 63.08 2.50

15 0.00095 0.00475 94,400 448 470,909 0.99494 5,504,938 58.32 2.57

20 0.00109 0.00544 93,951 511 468,525 0.99362 5,034,029 53.58 2.59

25 0.00149 0.00743 93,440 694 465,537 0.99180 4,565,503 48.86 2.60

30 0.00182 0.00906 92,746 840 461,719 0.98919 4,099,966 44.21 2.61

35 0.00254 0.01263 91,906 1,161 456,730 0.98669 3,638,247 39.59 2.59

40 0.00286 0.01422 90,745 1,290 450,652 0.98181 3,181,518 35.06 2.62

45 0.00463 0.02291 89,454 2,049 442,454 0.97354 2,730,866 30.53 2.65

50 0.00613 0.03022 87,405 2,641 430,748 0.96388 2,288,411 26.18 2.62

55 0.00890 0.04361 84,764 3,697 415,187 0.94404 1,857,664 21.92 2.67

60 0.01480 0.07157 81,067 5,802 391,952 0.90549 1,442,477 17.79 2.69

65 0.02611 0.12313 75,265 9,267 354,908 0.83618 1,050,524 13.96 2.69

70 0.04759 0.21398 65,998 14,122 296,766 0.72390 695,616 10.54 2.65

75 0.08530 0.35325 51,876 18,325 214,828 0.46138 398,850 7.69 2.57

80 0.18232 ... 33,551 33,551 184,022 ... 184,022 5.48 5.48

Kayah

Both sexes Kayah

0 0.05598 0.05357 100,000 5,357 95,696 0.94455 6,428,144 64.28 0.20

1 0.00211 0.00839 94,643 794 376,580 0.99026 6,332,448 66.91 1.49

5 0.00134 0.00669 93,849 628 467,675 0.99420 5,955,868 63.46 2.50

10 0.00098 0.00491 93,221 458 464,961 0.99358 5,488,193 58.87 2.50

15 0.00170 0.00845 92,763 784 461,978 0.99028 5,023,231 54.15 2.65

20 0.00210 0.01045 91,980 961 457,489 0.99087 4,561,253 49.59 2.49

25 0.00168 0.00839 91,018 764 453,310 0.98479 4,103,764 45.09 2.67

30 0.00477 0.02357 90,255 2,127 446,414 0.97517 3,650,454 40.45 2.72

35 0.00498 0.02458 88,127 2,166 435,331 0.97218 3,204,040 36.36 2.55

40 0.00638 0.03142 85,961 2,701 423,219 0.96697 2,768,709 32.21 2.56

45 0.00711 0.03497 83,260 2,912 409,241 0.95921 2,345,490 28.17 2.58

50 0.00983 0.04801 80,349 3,858 392,547 0.94379 1,936,248 24.1 2.62

55 0.01369 0.06630 76,491 5,071 370,481 0.91658 1,543,702 20.18 2.64

60 0.02193 0.10427 71,420 7,447 339,577 0.87058 1,173,221 16.43 2.65

65 0.03454 0.15960 63,973 10,210 295,630 0.79972 833,644 13.03 2.63

70 0.05683 0.24992 53,763 13,436 236,420 0.69192 538,014 10.01 2.59

75 0.09351 0.37932 40,326 15,297 163,585 0.45760 301,593 7.48 2.51

80 0.18136 ... 25,030 25,030 138,009 ... 138,009 5.51 5.51




Males Kayah

0 0.06195 0.05907 100,000 5,907 95,347 0.93903 5,909,712 59.10 0.21

1 0.00233 0.00928 94,093 873 374,167 0.98893 5,814,364 61.79 1.48

5 0.00154 0.00765 93,220 713 464,316 0.99358 5,440,197 58.36 2.50

10 0.00104 0.00519 92,507 480 461,333 0.99242 4,975,881 53.79 2.50

15 0.00222 0.01105 92,027 1,017 457,838 0.98559 4,514,548 49.06 2.74

20 0.00343 0.01699 91,010 1,546 451,241 0.98499 4,056,709 44.57 2.54

25 0.00275 0.01368 89,463 1,224 444,466 0.97482 3,605,468 40.30 2.67

30 0.00799 0.03922 88,240 3,461 433,274 0.95879 3,161,002 35.82 2.71

35 0.00819 0.04012 84,779 3,401 415,420 0.95814 2,727,728 32.17 2.51

40 0.00904 0.04420 81,377 3,597 398,029 0.95241 2,312,308 28.41 2.54

45 0.01071 0.05222 77,781 4,062 379,086 0.93867 1,914,279 24.61 2.58

50 0.01498 0.07232 73,719 5,331 355,838 0.91554 1,535,193 20.82 2.61

55 0.02083 0.09922 68,388 6,785 325,783 0.87718 1,179,355 17.25 2.62

60 0.03270 0.15168 61,602 9,344 285,771 0.81334 853,572 13.86 2.62

65 0.05135 0.22838 52,258 11,935 232,428 0.72246 567,801 10.87 2.58

70 0.08081 0.33652 40,324 13,570 167,920 0.60289 335,373 8.32 2.52

75 0.12480 0.47223 26,754 12,634 101,237 0.39543 167,453 6.26 2.43

80 0.21324 ... 14,120 14,120 66,216 ... 66,216 4.69 4.69

Females Kayah

0 0.04976 0.04784 100,000 4,784 96,142 0.95048 7,021,892 70.22 0.19

1 0.00182 0.00726 95,216 691 379,099 0.99165 6,925,750 72.74 1.45

5 0.00115 0.00572 94,525 541 471,272 0.99482 6,546,651 69.26 2.50

10 0.00093 0.00463 93,984 435 468,832 0.99470 6,075,379 64.64 2.50

15 0.00119 0.00593 93,549 555 466,349 0.99479 5,606,547 59.93 2.48

20 0.00087 0.00433 92,994 403 463,919 0.99628 5,140,198 55.27 2.39

25 0.00070 0.00350 92,591 324 462,194 0.99416 4,676,279 50.50 2.65

30 0.00176 0.00874 92,267 806 459,497 0.99054 4,214,085 45.67 2.72

35 0.00203 0.01009 91,461 923 455,151 0.98537 3,754,588 41.05 2.67

40 0.00396 0.01962 90,538 1,776 448,493 0.97982 3,299,438 36.44 2.64

45 0.00406 0.02012 88,762 1,786 439,442 0.97714 2,850,945 32.12 2.55

50 0.00536 0.02648 86,976 2,303 429,398 0.96865 2,411,503 27.73 2.62

55 0.00763 0.03746 84,673 3,172 415,938 0.95242 1,982,105 23.41 2.66

60 0.01241 0.06031 81,501 4,915 396,149 0.92102 1,566,167 19.22 2.69

65 0.02145 0.10221 76,586 7,828 364,861 0.86430 1,170,018 15.28 2.69

70 0.03862 0.17714 68,758 12,180 315,348 0.76858 805,158 11.71 2.66

75 0.06967 0.29844 56,578 16,885 242,370 0.50518 489,810 8.66 2.60

80 0.16041 ... 39,693 39,693 247,440 ... 247,440 6.23 6.23




Kayin

Both sexes Kayin

0 0.05309 0.05090 100,000 5,090 95,871 0.94736 6,208,093 62.08 0.19

1 0.00194 0.00771 94,910 732 377,810 0.99119 6,112,221 64.40 1.50

5 0.00118 0.00588 94,178 554 469,507 0.99503 5,734,411 60.89 2.50

10 0.00081 0.00406 93,624 380 467,172 0.99421 5,264,904 56.23 2.50

15 0.00166 0.00825 93,244 769 464,466 0.99004 4,797,732 51.45 2.72

20 0.00235 0.01167 92,475 1,079 459,840 0.98557 4,333,266 46.86 2.65

25 0.00349 0.01729 91,396 1,580 453,205 0.98115 3,873,427 42.38 2.61

30 0.00414 0.02052 89,816 1,843 444,662 0.97522 3,420,222 38.08 2.60

35 0.00597 0.02945 87,973 2,591 433,645 0.96760 2,975,560 33.82 2.60

40 0.00710 0.03490 85,382 2,980 419,597 0.96353 2,541,916 29.77 2.55

45 0.00800 0.03924 82,402 3,233 404,293 0.94947 2,122,319 25.76 2.61

50 0.01326 0.06431 79,169 5,091 383,865 0.92425 1,718,026 21.70 2.65

55 0.01852 0.08871 74,077 6,571 354,789 0.88961 1,334,161 18.01 2.63

60 0.02929 0.13693 67,506 9,244 315,625 0.83037 979,372 14.51 2.63

65 0.04640 0.20874 58,262 12,162 262,087 0.74363 663,747 11.39 2.60

70 0.07419 0.31365 46,101 14,459 194,895 0.62566 401,661 8.71 2.54

75 0.11659 0.44932 31,641 14,217 121,939 0.41026 206,766 6.53 2.45

80 0.20541 ... 17,424 17,424 84,827 ... 84,827 4.87 4.87

Males Kayin

0 0.06045 0.05769 100,000 5,769 95,433 0.94046 5,774,251 57.74 0.21

1 0.00225 0.00895 94,231 843 374,798 0.98931 5,678,818 60.26 1.48

5 0.00149 0.00742 93,388 693 465,206 0.99431 5,304,020 56.80 2.50

10 0.00079 0.00394 92,695 365 462,560 0.99338 4,838,814 52.20 2.500

15 0.00212 0.01053 92,329 972 459,498 0.98669 4,376,253 47.40 2.79

20 0.00323 0.01601 91,357 1,463 453,383 0.97967 3,916,756 42.87 2.67

25 0.00502 0.02479 89,895 2,228 444,167 0.97282 3,463,373 38.53 2.62

30 0.00600 0.02959 87,666 2,594 432,095 0.96477 3,019,206 34.44 2.60

35 0.00845 0.04143 85,072 3,525 416,874 0.95420 2,587,111 30.41 2.59

40 0.01017 0.04962 81,548 4,046 397,783 0.94849 2,170,237 26.61 2.54

45 0.01133 0.05514 77,501 4,273 377,293 0.92805 1,772,454 22.87 2.61

50 0.01933 0.09245 73,228 6,770 350,146 0.89204 1,395,161 19.05 2.64

55 0.02662 0.12510 66,458 8,314 312,346 0.84712 1,045,014 15.72 2.60

60 0.04101 0.18664 58,144 10,852 264,595 0.77465 732,668 12.6 2.59

65 0.06256 0.27113 47,292 12,822 204,968 0.67838 468,074 9.90 2.54

70 0.09469 0.38195 34,470 13,166 139,047 0.56024 263,106 7.63 2.47

75 0.14003 0.51201 21,304 10,908 77,899 0.37208 124,059 5.82 2.38

80 0.22522 ... 10,396 10,396 46,159 ... 46,159 4.44 4.44




Females Kayin

0 0.04559 0.04395 100,000 4,395 96,404 0.95458 6,672,227 66.72 0.18

1 0.00158 0.00630 95,605 602 380,886 0.99309 6,575,823 68.78 1.45

5 0.00086 0.00430 95,003 409 473,992 0.99576 6,194,937 65.21 2.50

10 0.00084 0.00417 94,594 394 471,985 0.99504 5,720,945 60.48 2.50

15 0.00122 0.00606 94,200 571 469,643 0.99314 5,248,960 55.72 2.63

20 0.00155 0.00771 93,629 722 466,421 0.99089 4,779,317 51.05 2.61

25 0.00212 0.01057 92,907 982 462,171 0.98867 4,312,896 46.42 2.59

30 0.00246 0.01224 91,925 1,125 456,936 0.98488 3,850,725 41.89 2.61

35 0.00370 0.01834 90,800 1,665 450,026 0.97975 3,393,789 37.38 2.61

40 0.00442 0.02187 89,135 1,949 440,912 0.97682 2,943,763 33.03 2.56

45 0.00510 0.02519 87,185 2,196 430,692 0.96815 2,502,851 28.71 2.62

50 0.00816 0.04004 84,989 3,403 416,974 0.95200 2,072,159 24.38 2.66

55 0.01179 0.05736 81,586 4,680 396,959 0.92694 1,655,185 20.29 2.66

60 0.01939 0.09277 76,906 7,135 367,959 0.87920 1,258,226 16.36 2.68

65 0.03351 0.15537 69,772 10,840 323,511 0.79818 890,267 12.76 2.66

70 0.05893 0.25821 58,931 15,217 258,219 0.67797 566,756 9.62 2.61

75 0.10019 0.40124 43,715 17,540 175,064 0.43260 308,537 7.06 2.52

80 0.19610 ... 26,175 26,175 133,473 ... 133,473 5.10 5.10

Chin

Both sexes Chin

0 0.07375 0.06985 100,000 6,985 94,715 0.92725 6,048,054 60.48 0.24

1 0.00334 0.01323 93,015 1,231 368,913 0.98494 5,953,339 64.00 1.44

5 0.00199 0.00992 91,784 911 456,646 0.99180 5,584,426 60.84 2.50

10 0.00130 0.00646 90,874 587 452,902 0.99152 5,127,780 56.43 2.50

15 0.00230 0.01144 90,287 1,033 449,062 0.98539 4,674,878 51.78 2.70

20 0.00351 0.01740 89,254 1,553 442,499 0.98290 4,225,817 47.35 2.57

25 0.00337 0.01669 87,701 1,464 434,930 0.98013 3,783,318 43.14 2.56

30 0.00479 0.02370 86,237 2,044 426,287 0.97372 3,348,387 38.83 2.60

35 0.00578 0.02851 84,193 2,400 415,083 0.96985 2,922,101 34.71 2.55

40 0.00642 0.03160 81,793 2,585 402,569 0.96787 2,507,017 30.65 2.53

45 0.00696 0.03426 79,208 2,714 389,636 0.95182 2,104,448 26.57 2.64

50 0.01344 0.06515 76,495 4,984 370,863 0.92428 1,714,812 22.42 2.67

55 0.01804 0.08646 71,511 6,183 342,782 0.89468 1,343,949 18.79 2.61

60 0.02735 0.12841 65,328 8,389 306,681 0.84256 1,001,168 15.33 2.62

65 0.04231 0.19199 56,939 10,932 258,398 0.76673 694,487 12.20 2.59

70 0.06565 0.28272 46,008 13,007 198,121 0.66226 436,088 9.48 2.55

75 0.10184 0.40490 33,000 13,362 131,208 0.44863 237,967 7.21 2.47

80 0.18395 ... 19,639 19,639 106,759 ... 106,759 5.44 5.44




Males Chin

0 0.08061 0.07608 100,000 7,608 94,379 0.92124 5,736,604 57.37 0.26

1 0.00353 0.01398 92,392 1,292 366,240 0.98330 5,642,224 61.07 1.42

5 0.00227 0.01130 91,100 1,029 452,928 0.99071 5,275,984 57.91 2.50

10 0.00146 0.00725 90,071 653 448,722 0.98967 4,823,056 53.55 2.50

15 0.00296 0.01468 89,418 1,313 444,087 0.98220 4,374,334 48.92 2.71

20 0.00415 0.02057 88,105 1,812 436,184 0.97731 3,930,247 44.61 2.60

25 0.00507 0.02506 86,293 2,163 426,287 0.96980 3,494,063 40.49 2.61

30 0.00726 0.03566 84,130 3,000 413,412 0.96140 3,067,777 36.46 2.59

35 0.00827 0.04053 81,130 3,288 397,454 0.95965 2,654,364 32.72 2.51

40 0.00815 0.03993 77,842 3,108 381,417 0.95998 2,256,910 28.99 2.49

45 0.00866 0.04243 74,734 3,171 366,152 0.94081 1,875,493 25.10 2.63

50 0.01653 0.07959 71,563 5,696 344,479 0.90828 1,509,341 21.09 2.66

55 0.02187 0.10389 65,867 6,843 312,883 0.87480 1,164,863 17.69 2.60

60 0.03257 0.15103 59,024 8,914 273,710 0.81872 851,979 14.43 2.60

65 0.04852 0.21700 50,110 10,874 224,093 0.74115 578,269 11.54 2.57

70 0.07299 0.30896 39,236 12,122 166,087 0.63759 354,175 9.03 2.52

75 0.10952 0.42773 27,114 11,597 105,896 0.43699 188,089 6.94 2.44

80 0.18878 ... 15,516 15,516 82,193 ... 82,193 5.30 5.30

Females Chin

0 0.06682 0.06359 100,000 6,359 95,172 0.93363 6,349,113 63.49 0.24

1 0.00305 0.01209 93,641 1,132 371,644 0.98664 6,253,941 66.79 1.42

5 0.00171 0.00849 92,509 785 460,581 0.99291 5,882,298 63.59 2.50

10 0.00114 0.00567 91,723 520 457,317 0.99328 5,421,717 59.11 2.50

15 0.00170 0.00845 91,203 771 454,243 0.98812 4,964,399 54.43 2.70

20 0.00300 0.01490 90,433 1,347 448,845 0.98720 4,510,156 49.87 2.54

25 0.00209 0.01039 89,085 926 443,098 0.98809 4,061,311 45.59 2.48

30 0.00285 0.01416 88,160 1,248 437,823 0.98369 3,618,212 41.04 2.62

35 0.00376 0.01864 86,911 1,620 430,682 0.97833 3,180,389 36.59 2.61

40 0.00498 0.02462 85,291 2,100 421,348 0.97469 2,749,707 32.24 2.57

45 0.00546 0.02696 83,191 2,243 410,684 0.96161 2,328,359 27.99 2.65

50 0.01075 0.05243 80,949 4,244 394,917 0.93829 1,917,675 23.69 2.68

55 0.01476 0.07128 76,704 5,467 370,545 0.91211 1,522,758 19.85 2.63

60 0.02285 0.10842 71,237 7,724 337,978 0.86410 1,152,213 16.17 2.64

65 0.03678 0.16912 63,513 10,741 292,046 0.78887 814,235 12.82 2.62

70 0.05986 0.26134 52,772 13,791 230,386 0.68372 522,190 9.90 2.57

75 0.09492 0.38358 38,981 14,952 157,519 0.46019 291,804 7.49 2.50

80 0.17894 ... 24,028 24,028 134,284 ... 134,284 5.59 5.59




Sagaing

Both sexes Sagaing

0 0.05884 0.05621 100,000 5,621 95,526 0.94176 6,574,574 65.75 0.20

1 0.00229 0.00911 94,379 860 375,352 0.99091 6,479,047 68.65 1.48

5 0.00086 0.00427 93,519 399 466,598 0.99608 6,103,695 65.27 2.50

10 0.00072 0.00357 93,120 332 464,768 0.99535 5,637,097 60.54 2.50

15 0.00124 0.00617 92,787 572 462,608 0.99265 5,172,329 55.74 2.68

20 0.00171 0.00851 92,215 785 459,206 0.99024 4,709,721 51.07 2.62

25 0.00223 0.01109 91,430 1,014 454,725 0.98733 4,250,514 46.49 2.61

30 0.00293 0.01453 90,416 1,314 448,963 0.98239 3,795,789 41.98 2.63

35 0.00422 0.02087 89,102 1,860 441,056 0.97712 3,346,826 37.56 2.60

40 0.00502 0.02481 87,243 2,164 430,967 0.97197 2,905,770 33.31 2.58

45 0.00637 0.03138 85,078 2,670 418,887 0.96693 2,474,803 29.09 2.56

50 0.00725 0.03564 82,409 2,937 405,034 0.95486 2,055,916 24.95 2.61

55 0.01181 0.05747 79,472 4,567 386,752 0.92694 1,650,882 20.77 2.68

60 0.01916 0.09170 74,904 6,869 358,497 0.88275 1,264,130 16.88 2.67

65 0.03187 0.14826 68,036 10,087 316,462 0.81107 905,634 13.31 2.65

70 0.05385 0.23850 57,949 13,821 256,673 0.70129 589,171 10.17 2.61

75 0.09136 0.37268 44,128 16,446 180,003 0.45863 332,498 7.53 2.53

80 0.18153 ... 27,682 27,682 152,495 ... 152,495 5.51 5.51

Males Sagaing

0 0.06620 0.06296 100,000 6,296 95,111 0.93496 6,095,955 60.96 0.22

1 0.00259 0.01029 93,704 964 372,370 0.98960 6,000,844 64.04 1.46

5 0.00093 0.00465 92,740 431 462,621 0.99570 5,628,474 60.69 2.50

10 0.00079 0.00395 92,309 365 460,631 0.99432 5,165,853 55.96 2.50

15 0.00164 0.00817 91,944 751 458,016 0.98982 4,705,222 51.17 2.73

20 0.00245 0.01218 91,193 1,111 453,352 0.98550 4,247,206 46.57 2.65

25 0.00344 0.01704 90,082 1,535 446,777 0.97981 3,793,853 42.12 2.63

30 0.00481 0.02376 88,547 2,104 437,755 0.97138 3,347,076 37.80 2.63

35 0.00682 0.03355 86,443 2,900 425,227 0.96367 2,909,321 33.66 2.59

40 0.00794 0.03893 83,543 3,252 409,777 0.95647 2,484,094 29.73 2.56

45 0.00982 0.04793 80,291 3,848 391,941 0.95227 2,074,317 25.84 2.53

50 0.01002 0.04894 76,442 3,741 373,234 0.93581 1,682,376 22.01 2.60

55 0.01757 0.08439 72,701 6,135 349,277 0.89304 1,309,142 18.01 2.68

60 0.02844 0.13328 66,566 8,872 311,918 0.83254 959,866 14.42 2.64

65 0.04634 0.20860 57,694 12,035 259,685 0.74083 647,948 11.23 2.61

70 0.07599 0.32016 45,659 14,618 192,382 0.61526 388,263 8.50 2.54

75 0.12193 0.46495 31,041 14,432 118,366 0.39573 195,882 6.31 2.45

80 0.21426 ... 16,608 16,608 77,516 ... 77,516 4.67 4.67




Females Sagaing

0 0.05143 0.04939 100,000 4,939 96,040 0.94883 7,043,118 70.43 0.20

1 0.00193 0.00769 95,061 731 378,375 0.99224 6,947,078 73.08 1.44

5 0.00078 0.00388 94,330 366 470,735 0.99646 6,568,703 69.64 2.50

10 0.00064 0.00320 93,964 301 469,068 0.99629 6,097,968 64.90 2.50

15 0.00089 0.00442 93,663 414 467,328 0.99500 5,628,899 60.10 2.61

20 0.00111 0.00553 93,249 516 464,992 0.99412 5,161,571 55.35 2.57

25 0.00124 0.00617 92,734 572 462,259 0.99366 4,696,579 50.65 2.54

30 0.00134 0.00669 92,161 617 459,328 0.99175 4,234,320 45.94 2.60

35 0.00203 0.01010 91,545 925 455,536 0.98852 3,774,993 41.24 2.63

40 0.00260 0.01291 90,620 1,170 450,308 0.98487 3,319,456 36.63 2.61

45 0.00357 0.01772 89,450 1,585 443,497 0.97893 2,869,148 32.08 2.63

50 0.00505 0.02494 87,865 2,191 434,151 0.97017 2,425,651 27.61 2.64

55 0.00730 0.03587 85,674 3,073 421,200 0.95397 1,991,500 23.25 2.67

60 0.01210 0.05888 82,601 4,864 401,813 0.92198 1,570,300 19.01 2.70

65 0.02141 0.10201 77,737 7,930 370,462 0.86284 1,168,486 15.03 2.70

70 0.03951 0.18090 69,807 12,628 319,650 0.76141 798,024 11.43 2.67

75 0.07288 0.31021 57,179 17,738 243,386 0.49122 478,374 8.37 2.60

80 0.16785 ... 39,442 39,442 234,988 ... 234,988 5.96 5.96

Tanintharyi

Both sexes Tanintharyi

0 0.06818 0.06477 100,000 6,477 95,004 0.93267 6,553,035 65.53 0.23

1 0.00293 0.01162 93,523 1,087 371,329 0.98913 6,458,030 69.05 1.46

5 0.00079 0.00396 92,436 366 461,266 0.99630 6,086,701 65.85 2.50

10 0.00069 0.00344 92,070 317 459,559 0.99527 5,625,435 61.10 2.50

15 0.00129 0.00644 91,753 591 457,384 0.99301 5,165,875 56.30 2.66

20 0.00149 0.00744 91,163 678 454,189 0.99098 4,708,492 51.65 2.61

25 0.00217 0.01081 90,484 978 450,090 0.98799 4,254,303 47.02 2.62

30 0.00267 0.01325 89,506 1,186 444,684 0.98457 3,804,213 42.50 2.60

35 0.00360 0.01783 88,320 1,575 437,821 0.98013 3,359,529 38.04 2.60

40 0.00446 0.02207 86,746 1,914 429,123 0.97449 2,921,707 33.68 2.59

45 0.00593 0.02925 84,831 2,481 418,176 0.96772 2,492,584 29.38 2.59

50 0.00730 0.03587 82,350 2,954 404,677 0.95722 2,074,408 25.19 2.61

55 0.01061 0.05176 79,396 4,110 387,365 0.93379 1,669,732 21.03 2.66

60 0.01753 0.08421 75,286 6,340 361,716 0.89103 1,282,367 17.03 2.68

65 0.02983 0.13943 68,946 9,613 322,300 0.81841 920,651 13.35 2.67

70 0.05253 0.23355 59,333 13,857 263,775 0.70233 598,351 10.08 2.63

75 0.09255 0.37701 45,476 17,145 185,256 0.44629 334,576 7.36 2.54

80 0.18973 ... 28,331 28,331 149,319 ... 149,319 5.27 5.27




Males Tanintharyi

0 0.07510 0.07108 100,000 7,108 94,647 0.92647 6,219,636 62.20 0.25

1 0.00316 0.01253 92,892 1,164 368,587 0.98788 6,124,989 65.94 1.44

5 0.00089 0.00446 91,728 409 457,618 0.99590 5,756,402 62.76 2.50

10 0.00075 0.00373 91,319 341 455,743 0.99429 5,298,785 58.03 2.50

15 0.00168 0.00835 90,978 760 453,143 0.99083 4,843,042 53.23 2.70

20 0.00197 0.00978 90,219 882 448,988 0.9880 4,389,899 48.66 2.61

25 0.00295 0.01463 89,336 1,307 443,599 0.98285 3,940,911 44.11 2.64

30 0.00398 0.01973 88,029 1,737 435,992 0.97743 3,497,313 39.73 2.61

35 0.00514 0.02538 86,293 2,190 426,150 0.97271 3,061,321 35.48 2.57

40 0.00600 0.02955 84,102 2,485 414,521 0.96496 2,635,171 31.33 2.59

45 0.00838 0.04106 81,617 3,351 399,996 0.95562 2,220,650 27.21 2.59

50 0.00985 0.04813 78,266 3,767 382,245 0.94318 1,820,653 23.26 2.59

55 0.01412 0.06832 74,499 5,090 360,525 0.91323 1,438,409 19.31 2.65

60 0.02311 0.10964 69,409 7,610 329,244 0.86027 1,077,883 15.53 2.66

65 0.03846 0.17629 61,799 10,895 283,239 0.77628 748,640 12.11 2.64

70 0.06516 0.28145 50,905 14,327 219,872 0.65301 465,400 9.14 2.58

75 0.10907 0.42815 36,578 15,661 143,578 0.41523 245,529 6.71 2.49

80 0.20517 ... 20,917 20,917 101,951 ... 101,951 4.87 4.87

Females Tanintharyi

0 0.06119 0.05842 100,000 5,842 95,474 0.93919 6,890,221 68.90 0.23

1 0.00261 0.01038 94,158 977 374,119 0.99044 6,794,747 72.16 1.43

5 0.00069 0.00344 93,181 321 465,102 0.99671 6,420,628 68.91 2.50

10 0.00063 0.00314 92,860 292 463,572 0.99623 5,955,526 64.13 2.50

15 0.00092 0.00461 92,569 427 461,822 0.99504 5,491,955 59.33 2.61

20 0.00106 0.00531 92,142 489 459,531 0.99369 5,030,133 54.59 2.59

25 0.00146 0.00727 91,653 666 456,633 0.99289 4,570,602 49.87 2.55

30 0.00140 0.00699 90,986 636 453,386 0.99155 4,113,969 45.22 2.57

35 0.00208 0.01037 90,350 937 449,556 0.98728 3,660,582 40.52 2.66

40 0.00305 0.01514 89,413 1,354 443,840 0.98315 3,211,026 35.91 2.62

45 0.00376 0.01864 88,060 1,641 436,360 0.97833 2,767,186 31.42 2.60

50 0.00514 0.02538 86,418 2,193 426,903 0.96943 2,330,827 26.97 2.63

55 0.00756 0.03713 84,225 3,127 413,850 0.95190 1,903,924 22.61 2.67

60 0.01277 0.06205 81,098 5,032 393,943 0.91695 1,490,074 18.37 2.71

65 0.02306 0.10949 76,065 8,328 361,226 0.85162 1,096,131 14.41 2.71

70 0.04334 0.19685 67,737 13,334 307,627 0.74039 734,905 10.85 2.67

75 0.08071 0.33791 54,403 18,383 227,764 0.46694 427,278 7.85 2.59

80 0.18054 ... 36,020 36,020 199,514 ... 199,514 5.54 5.54




Bago

Both sexes Bago

0 0.06360 0.06058 100,000 6,058 95,255 0.93711 6,520,170 65.20 0.22

1 0.00261 0.01038 93,942 975 373,301 0.99010 6,424,916 68.39 1.47

5 0.00079 0.00395 92,967 367 463,916 0.99656 6,051,614 65.09 2.50

10 0.00059 0.00293 92,600 271 462,320 0.99571 5,587,698 60.34 2.50

15 0.00125 0.00621 92,328 573 460,335 0.99254 5,125,378 55.51 2.72

20 0.00172 0.00858 91,755 787 456,902 0.99016 4,665,043 50.84 2.62

25 0.00227 0.01128 90,968 1,026 452,405 0.98638 4,208,140 46.26 2.63

30 0.00326 0.01616 89,942 1,453 446,245 0.98172 3,755,736 41.76 2.62

35 0.00411 0.02036 88,488 1,802 438,086 0.97733 3,309,491 37.40 2.58

40 0.00506 0.02498 86,687 2,165 428,154 0.97333 2,871,404 33.12 2.56

45 0.00584 0.02877 84,521 2,432 416,736 0.96633 2,443,250 28.91 2.59

50 0.00810 0.03976 82,089 3,264 402,704 0.95263 2,026,515 24.69 2.63

55 0.01167 0.05679 78,826 4,477 383,629 0.92769 1,623,811 20.60 2.65

60 0.01913 0.09159 74,349 6,810 355,890 0.88223 1,240,182 16.68 2.67

65 0.03221 0.14974 67,539 10,113 313,978 0.80739 884,292 13.09 2.65

70 0.05549 0.24496 57,426 14,067 253,504 0.69260 570,314 9.93 2.61

75 0.09494 0.38446 43,359 16,670 175,577 0.44579 316,809 7.31 2.53

80 0.18897 ... 26,689 26,689 141,232 ... 141,232 5.29 5.29

Males Bago

0 0.07479 0.07080 100,000 7,080 94,662 0.92675 6,071,699 60.72 0.25

1 0.00315 0.01248 92,920 1,160 368,711 0.98823 5,977,037 64.32 1.44

5 0.00077 0.00384 91,760 352 457,921 0.99635 5,608,326 61.12 2.50

10 0.00069 0.00346 91,408 316 456,249 0.99497 5,150,405 56.35 2.50

15 0.00147 0.00734 91,092 669 453,953 0.99059 4,694,156 51.53 2.75

20 0.00231 0.01149 90,423 1,039 449,679 0.98652 4,240,203 46.89 2.65

25 0.00317 0.01574 89,384 1,407 443,618 0.98011 3,790,524 42.41 2.65

30 0.00496 0.02449 87,977 2,155 434,795 0.97173 3,346,906 38.04 2.64

35 0.00644 0.03169 85,823 2,720 422,504 0.96593 2,912,111 33.93 2.57

40 0.00739 0.03631 83,103 3,017 408,111 0.96098 2,489,607 29.96 2.55

45 0.00866 0.04239 80,085 3,395 392,188 0.95170 2,081,496 25.99 2.57

50 0.01145 0.05571 76,691 4,272 373,246 0.93376 1,689,307 22.03 2.61

55 0.01654 0.07958 72,418 5,763 348,524 0.89887 1,316,061 18.17 2.65

60 0.02719 0.12780 66,655 8,519 313,279 0.83783 967,537 14.52 2.65

65 0.04513 0.20375 58,137 11,845 262,474 0.74436 654,258 11.25 2.62

70 0.07543 0.31836 46,291 14,737 195,376 0.61485 391,784 8.46 2.55

75 0.12301 0.46831 31,554 14,777 120,127 0.38838 196,408 6.22 2.45

80 0.21994 ... 16,777 16,777 76,281 ... 76,281 4.55 4.55




Females Bago

0 0.05234 0.05024 100,000 5,024 95,984 0.94791 6,974,688 69.75 0.20

1 0.00200 0.00796 94,976 756 377,970 0.99195 6,878,703 72.43 1.44

5 0.00082 0.00407 94,220 383 470,141 0.99676 6,500,733 69.00 2.50

10 0.00048 0.00241 93,837 226 468,617 0.99641 6,030,592 64.27 2.50

15 0.00104 0.00517 93,610 484 466,935 0.99425 5,561,974 59.42 2.69

20 0.00123 0.00611 93,126 569 464,252 0.99324 5,095,039 54.71 2.57

25 0.00150 0.00745 92,557 690 461,115 0.99185 4,630,788 50.03 2.57

30 0.00178 0.00886 91,868 814 457,358 0.99047 4,169,673 45.39 2.57

35 0.00209 0.01042 91,054 949 453,001 0.98727 3,712,315 40.77 2.61

40 0.00306 0.01520 90,105 1,370 447,236 0.98391 3,259,314 36.17 2.60

45 0.00345 0.01713 88,736 1,520 440,040 0.97883 2,812,079 31.69 2.61

50 0.00530 0.02618 87,215 2,283 430,724 0.96856 2,372,038 27.20 2.66

55 0.00769 0.03775 84,932 3,206 417,182 0.95151 1,941,314 22.86 2.67

60 0.01278 0.06207 81,726 5,073 396,955 0.91773 1,524,132 18.65 2.70

65 0.02263 0.10757 76,653 8,246 364,297 0.85559 1,127,177 14.70 2.70

70 0.04174 0.19016 68,408 13,008 311,689 0.75049 762,881 11.15 2.67

75 0.07655 0.32324 55,399 17,907 233,918 0.48156 451,192 8.14 2.59

80 0.17256 ... 37,492 37,492 217,274 ... 217,274 5.80 5.80

Magway

Both sexes Magway

0 0.08881 0.08349 100,000 8,349 94,010 0.91264 6,226,821 62.27 0.28

1 0.00456 0.01803 91,651 1,652 362,310 0.98410 6,132,811 66.91 1.40

5 0.00083 0.00413 89,999 372 449,063 0.99612 5,770,501 64.12 2.50

10 0.00073 0.00363 89,627 325 447,321 0.99542 5,321,437 59.37 2.50

15 0.00120 0.00599 89,301 535 445,271 0.99251 4,874,117 54.58 2.69

20 0.00182 0.00904 88,767 802 441,936 0.98970 4,428,846 49.89 2.64

25 0.00235 0.01168 87,964 1,027 437,385 0.98574 3,986,910 45.32 2.63

30 0.00346 0.01718 86,937 1,494 431,146 0.98000 3,549,525 40.83 2.63

35 0.00458 0.02264 85,443 1,934 422,525 0.97586 3,118,378 36.50 2.58

40 0.00520 0.02569 83,509 2,145 412,323 0.97120 2,695,853 32.28 2.57

45 0.00665 0.03271 81,363 2,661 400,449 0.96142 2,283,530 28.07 2.61

50 0.00931 0.04553 78,702 3,583 385,000 0.94598 1,883,081 23.93 2.63

55 0.01330 0.06449 75,119 4,844 364,202 0.91819 1,498,081 19.94 2.65

60 0.02169 0.10320 70,274 7,252 334,407 0.86888 1,133,879 16.14 2.66

65 0.03575 0.16482 63,022 10,387 290,559 0.79177 799,472 12.69 2.64

70 0.05969 0.26090 52,635 13,732 230,057 0.67769 508,913 9.67 2.59

75 0.09928 0.39789 38,902 15,479 155,907 0.44091 278,856 7.17 2.51

80 0.19051 ... 23,423 23,423 122,950 ... 122,950 5.25 5.25




Males Magway

0 0.10243 0.09574 100,000 9,574 93,468 0.90062 5,708,332 57.08 0.32

1 0.00517 0.02041 90,426 1,846 356,840 0.98133 5,614,864 62.09 1.36

5 0.00091 0.00453 88,580 401 441,899 0.99576 5,258,024 59.36 2.50

10 0.00079 0.00394 88,179 347 440,027 0.99471 4,816,125 54.62 2.50

15 0.00148 0.00738 87,832 648 437,700 0.98982 4,376,098 49.82 2.75

20 0.00265 0.01319 87,184 1,150 433,245 0.98469 3,938,398 45.17 2.68

25 0.00355 0.01758 86,034 1,512 426,610 0.97772 3,505,153 40.74 2.65

30 0.00559 0.02761 84,521 2,334 417,105 0.96772 3,078,543 36.42 2.64

35 0.00744 0.03654 82,187 3,003 403,640 0.96105 2,661,438 32.38 2.57

40 0.00843 0.04132 79,184 3,272 387,919 0.95423 2,257,798 28.51 2.55

45 0.01050 0.05121 75,912 3,887 370,165 0.94143 1,869,879 24.63 2.58

50 0.01394 0.06745 72,025 4,858 348,487 0.92029 1,499,713 20.82 2.60

55 0.01992 0.09513 67,167 6,390 320,710 0.88041 1,151,227 17.14 2.63

60 0.03222 0.14968 60,777 9,097 282,357 0.81303 830,516 13.66 2.63

65 0.05216 0.23168 51,680 11,973 229,565 0.71523 548,160 10.61 2.59

70 0.08429 0.34853 39,707 13,839 164,192 0.58712 318,594 8.02 2.52

75 0.13235 0.49322 25,868 12,759 96,400 0.37566 154,403 5.97 2.42

80 0.22601 ... 13,109 13,109 58,002 ... 58,002 4.42 4.42

Females Magway

0 0.07530 0.07135 100,000 7,135 94,749 0.92523 6,749,433 67.49 0.26

1 0.00377 0.01493 92,865 1,386 367,866 0.98687 6,654,684 71.66 1.41

5 0.00075 0.00373 91,479 341 456,540 0.99647 6,286,818 68.72 2.50

10 0.00067 0.00333 91,137 303 454,928 0.99605 5,830,279 63.97 2.50

15 0.00096 0.00480 90,834 436 453,130 0.99466 5,375,351 59.18 2.62

20 0.00117 0.00585 90,398 529 450,708 0.99357 4,922,221 54.45 2.58

25 0.00141 0.00705 89,869 634 447,812 0.99217 4,471,513 49.76 2.58

30 0.00175 0.00873 89,235 779 444,306 0.98991 4,023,701 45.09 2.60

35 0.00231 0.01148 88,456 1,015 439,824 0.98776 3,579,395 40.47 2.58

40 0.00263 0.01308 87,441 1,144 434,440 0.98503 3,139,570 35.91 2.58

45 0.00353 0.01750 86,297 1,510 427,936 0.97787 2,705,130 31.35 2.65

50 0.00559 0.02759 84,787 2,339 418,465 0.96684 2,277,194 26.86 2.66

55 0.00810 0.03976 82,448 3,278 404,588 0.94898 1,858,730 22.54 2.67

60 0.01346 0.06526 79,170 5,167 383,945 0.91366 1,454,142 18.37 2.70

65 0.02377 0.11269 74,003 8,339 350,796 0.84927 1,070,197 14.46 2.70

70 0.04360 0.19783 65,664 12,990 297,921 0.74202 719,401 10.96 2.66

75 0.07926 0.33266 52,673 17,522 221,064 0.47551 421,479 8.00 2.59

80 0.17539 ... 35,151 35,151 200,416 ... 200,416 5.70 5.70




Mandalay

Both sexes Mandalay

0 0.05231 0.05018 100,000 5,018 95,919 0.94811 6,489,291 64.89 0.19

1 0.00190 0.00755 94,982 717 378,136 0.99208 6,393,372 67.31 1.50

5 0.00087 0.00434 94,265 409 470,302 0.99590 6,015,235 63.81 2.50

10 0.00077 0.00386 93,856 362 468,373 0.99533 5,544,934 59.08 2.50

15 0.00117 0.00585 93,493 547 466,184 0.99305 5,076,560 54.30 2.65

20 0.00163 0.00814 92,947 757 462,944 0.99038 4,610,376 49.60 2.64

25 0.00229 0.01139 92,190 1,050 458,491 0.98562 4,147,432 44.99 2.66

30 0.00357 0.01771 91,140 1,614 451,899 0.97934 3,688,941 40.48 2.65

35 0.00477 0.02356 89,526 2,109 442,560 0.97349 3,237,043 36.16 2.60

40 0.00596 0.02939 87,417 2,569 430,829 0.96849 2,794,482 31.97 2.57

45 0.00693 0.03410 84,847 2,893 417,255 0.95994 2,363,653 27.86 2.59

50 0.00969 0.04738 81,954 3,883 400,541 0.94392 1,946,398 23.75 2.62

55 0.01379 0.06678 78,071 5,214 378,078 0.91563 1,545,857 19.80 2.65

60 0.02232 0.10606 72,858 7,727 346,179 0.86580 1,167,779 16.03 2.66

65 0.03654 0.16814 65,130 10,951 299,720 0.78831 821,601 12.61 2.63

70 0.06063 0.26440 54,179 14,325 236,273 0.67508 521,881 9.63 2.58

75 0.09979 0.39937 39,854 15,917 159,503 0.44153 285,608 7.17 2.50

80 0.18982 ... 23,938 23,938 126,105 ... 126,105 5.27 5.27

Males Mandalay

0 0.06036 0.05761 100,000 5,761 95,438 0.94054 5,968,488 59.68 0.21

1 0.00225 0.00895 94,239 843 374,830 0.99061 5,873,050 62.32 1.48

5 0.00097 0.00483 93,396 451 465,850 0.99561 5,498,220 58.87 2.50

10 0.00079 0.00395 92,944 367 463,804 0.99469 5,032,370 54.14 2.50

15 0.00146 0.00729 92,577 675 461,341 0.99099 4,568,566 49.35 2.71

20 0.0022 0.01094 91,902 1,005 457,185 0.98579 4,107,225 44.69 2.69

25 0.00365 0.01809 90,897 1,644 450,688 0.97706 3,650,039 40.16 2.69

30 0.00571 0.02815 89,253 2,512 440,350 0.96678 3,199,351 35.85 2.65

35 0.00780 0.03827 86,740 3,320 425,720 0.95687 2,759,001 31.81 2.60

40 0.00977 0.04771 83,421 3,980 407,359 0.94958 2,333,280 27.97 2.55

45 0.01101 0.05363 79,441 4,260 386,820 0.93862 1,925,922 24.24 2.56

50 0.01474 0.07119 75,180 5,352 363,077 0.91601 1,539,102 20.47 2.60

55 0.02100 0.10003 69,828 6,985 332,581 0.87465 1,176,025 16.84 2.63

60 0.03380 0.15645 62,843 9,832 290,891 0.80548 843,445 13.42 2.63

65 0.05431 0.24006 53,011 12,726 234,307 0.70663 552,554 10.42 2.58

70 0.08693 0.35729 40,286 14,394 165,569 0.57914 318,247 7.90 2.51

75 0.13510 0.50032 25,892 12,954 95,887 0.37196 152,678 5.90 2.41

80 0.22781 ... 12,938 12,938 56,790 ... 56,790 4.39 4.39




Females Mandalay

0 0.04415 0.04260 100,000 4,260 96,497 0.95595 7,017,172 70.17 0.18

1 0.00153 0.00608 95,740 582 381,478 0.99351 6,920,675 72.29 1.45

5 0.00077 0.00385 95,158 366 474,874 0.99619 6,539,196 68.72 2.50

10 0.00075 0.00376 94,792 356 473,067 0.99594 6,064,323 63.98 2.50

15 0.00091 0.00453 94,435 428 471,144 0.99482 5,591,256 59.21 2.59

20 0.00116 0.00578 94,007 543 468,704 0.99427 5,120,112 54.47 2.55

25 0.00116 0.00576 93,464 538 466,020 0.99287 4,651,408 49.77 2.59

30 0.00177 0.00879 92,926 817 462,695 0.99002 4,185,388 45.04 2.63

35 0.00224 0.01112 92,109 1,024 458,079 0.98750 3,722,693 40.42 2.59

40 0.00283 0.01403 91,085 1,278 452,351 0.98418 3,264,614 35.84 2.60

45 0.00365 0.01810 89,807 1,626 445,196 0.97728 2,812,263 31.31 2.64

50 0.00572 0.02821 88,181 2,488 435,079 0.96619 2,367,067 26.84 2.66

55 0.00825 0.04046 85,694 3,467 420,368 0.94822 1,931,988 22.55 2.66

60 0.01363 0.06606 82,226 5,432 398,603 0.91290 1,511,620 18.38 2.69

65 0.02392 0.11334 76,795 8,704 363,886 0.84897 1,113,017 14.49 2.69

70 0.04356 0.19762 68,091 13,456 308,929 0.74307 749,131 11.00 2.66

75 0.07865 0.33046 54,635 18,055 229,555 0.47852 440,202 8.06 2.58

80 0.17366 ... 36,580 36,580 210,647 ... 210,647 5.76 5.76

Mon

Both sexes Mon

0 0.03819 0.03699 100,000 3,699 96,852 0.96192 6,350,259 63.50 0.15

1 0.00116 0.00462 96,301 445 384,110 0.99456 6,253,407 64.94 1.54

5 0.00078 0.00390 95,856 374 478,346 0.99636 5,869,297 61.23 2.50

10 0.00068 0.00337 95,482 322 476,607 0.99504 5,390,951 56.46 2.50

15 0.00147 0.00732 95,160 697 474,244 0.99034 4,914,344 51.64 2.76

20 0.00242 0.01204 94,464 1,137 469,661 0.98590 4,440,100 47.00 2.66

25 0.00329 0.01631 93,327 1,522 463,041 0.97984 3,970,439 42.54 2.64

30 0.00490 0.02422 91,804 2,224 453,705 0.97336 3,507,398 38.21 2.61

35 0.00582 0.02867 89,581 2,568 441,618 0.96935 3,053,693 34.09 2.55

40 0.00667 0.03283 87,013 2,857 428,081 0.96418 2,612,076 30.02 2.56

45 0.00813 0.03987 84,156 3,355 412,745 0.95177 2,183,994 25.95 2.61

50 0.01200 0.05835 80,801 4,715 392,839 0.93048 1,771,249 21.92 2.63

55 0.01727 0.08299 76,086 6,314 365,529 0.89539 1,378,410 18.12 2.64

60 0.02796 0.13116 69,772 9,151 327,293 0.83527 1,012,881 14.52 2.64

65 0.04549 0.20516 60,620 12,437 273,379 0.74471 685,588 11.31 2.61

70 0.07476 0.31590 48,183 15,221 203,587 0.61915 412,209 8.55 2.55

75 0.12068 0.46148 32,962 15,211 126,050 0.39579 208,621 6.33 2.45

80 0.21498 ... 17,751 17,751 82,571 ... 82,571 4.65 4.65




Males Mon

0 0.04318 0.04167 100,000 4,167 96,509 0.95716 5,823,715 58.24 0.16

1 0.00134 0.00533 95,833 511 382,069 0.99354 5,727,206 59.76 1.53

5 0.00095 0.00472 95,322 450 475,486 0.99573 5,345,136 56.07 2.50

10 0.00077 0.00382 94,872 362 473,455 0.99403 4,869,650 51.33 2.50

15 0.00188 0.00934 94,510 883 470,627 0.98637 4,396,195 46.52 2.82

20 0.00367 0.01818 93,627 1,702 464,212 0.97873 3,925,568 41.93 2.70

25 0.00496 0.02453 91,925 2,255 454,339 0.96807 3,461,355 37.65 2.66

30 0.00813 0.03990 89,670 3,578 439,834 0.95621 3,007,016 33.53 2.62

35 0.00956 0.04671 86,092 4,021 420,575 0.95016 2,567,182 29.82 2.54

40 0.01092 0.05319 82,071 4,365 399,615 0.94285 2,146,608 26.16 2.54

45 0.01291 0.06262 77,706 4,866 376,779 0.92520 1,746,992 22.48 2.59

50 0.01873 0.08966 72,840 6,531 348,595 0.89446 1,370,214 18.81 2.61

55 0.02652 0.12472 66,309 8,270 311,804 0.84546 1,021,618 15.41 2.61

60 0.04204 0.19093 58,039 11,081 263,617 0.76687 709,814 12.23 2.60

65 0.06578 0.28318 46,957 13,297 202,161 0.66211 446,197 9.50 2.55

70 0.10140 0.40322 33,660 13,572 133,853 0.53634 244,036 7.25 2.46

75 0.15106 0.53988 20,088 10,845 71,791 0.34844 110,183 5.49 2.36

80 0.24075 ... 9,243 9,243 38,392 ... 38,392 4.15 4.15

Females Mon

0 0.03297 0.03207 100,000 3,207 97,262 0.96692 6,906,954 69.07 0.15

1 0.00100 0.00399 96,793 386 386,196 0.99554 6,809,692 70.35 1.47

5 0.00061 0.00305 96,407 294 481,299 0.99701 6,423,496 66.63 2.50

10 0.00058 0.00292 96,113 281 479,862 0.99599 5,942,198 61.83 2.50

15 0.00110 0.00549 95,832 526 477,937 0.99380 5,462,335 57.00 2.67

20 0.00137 0.00682 95,306 650 474,976 0.99193 4,984,398 52.3 2.61

25 0.00188 0.00937 94,656 887 471,144 0.98990 4,509,422 47.64 2.59

30 0.00217 0.01078 93,769 1,011 466,387 0.98804 4,038,278 43.07 2.57

35 0.00267 0.01327 92,758 1,231 460,810 0.98542 3,571,891 38.51 2.58

40 0.00324 0.01609 91,527 1,473 454,090 0.98170 3,111,080 33.99 2.59

45 0.00428 0.02118 90,055 1,907 445,778 0.97338 2,656,991 29.50 2.64

50 0.00673 0.03311 88,147 2,919 433,912 0.95985 2,211,213 25.09 2.66

55 0.00995 0.04861 85,229 4,143 416,493 0.93708 1,777,301 20.85 2.67

60 0.01684 0.08106 81,086 6,573 390,287 0.89224 1,360,808 16.78 2.70

65 0.03020 0.14112 74,513 10,515 348,228 0.81211 970,521 13.02 2.69

70 0.05551 0.24531 63,998 15,699 282,800 0.68708 622,293 9.72 2.63

75 0.09876 0.39731 48,298 19,189 194,308 0.42765 339,493 7.03 2.54

80 0.20050 ... 29,109 29,109 145,185 ... 145,185 4.99 4.99




Rakhine

Both sexes Rakhine

0 0.05842 0.05582 100,000 5,582 95,551 0.94218 6,547,258 65.47 0.20

1 0.00226 0.00899 94,418 849 375,537 0.99027 6,451,707 68.33 1.48

5 0.00115 0.00573 93,569 536 466,506 0.99489 6,076,170 64.94 2.50

10 0.00090 0.00448 93,033 417 464,123 0.99440 5,609,664 60.30 2.50

15 0.00142 0.00710 92,616 658 461,524 0.99215 5,145,541 55.56 2.63

20 0.00172 0.00856 91,959 787 457,902 0.99001 4,684,017 50.94 2.60

25 0.00231 0.01148 91,172 1,047 453,325 0.98790 4,226,115 46.35 2.58

30 0.00259 0.01288 90,125 1,161 447,841 0.98424 3,772,790 41.86 2.60

35 0.00388 0.01920 88,964 1,708 440,782 0.97759 3,324,948 37.37 2.64

40 0.00517 0.02554 87,256 2,229 430,905 0.97208 2,884,166 33.05 2.59

45 0.00623 0.03071 85,027 2,611 418,876 0.96323 2,453,261 28.85 2.60

50 0.00899 0.04402 82,416 3,628 403,475 0.94817 2,034,385 24.68 2.63

55 0.01260 0.06117 78,788 4,819 382,564 0.92363 1,630,910 20.70 2.64

60 0.01991 0.09510 73,969 7,034 353,347 0.88002 1,248,346 16.88 2.65

65 0.03232 0.15014 66,934 10,050 310,951 0.81017 894,999 13.37 2.64

70 0.05376 0.23808 56,885 13,543 251,922 0.70433 584,048 10.27 2.60

75 0.08946 0.36622 43,342 15,873 177,435 0.46576 332,126 7.66 2.53

80 0.17757 ... 27,469 27,469 154,691 ... 154,691 5.63 5.63

Males Rakhine

0 0.06465 0.06154 100,000 6,154 95,196 0.93645 6,160,401 61.60 0.22

1 0.00249 0.00991 93,846 930 373,029 0.98893 6,065,205 64.63 1.47

5 0.00133 0.00663 92,916 616 463,040 0.99437 5,692,176 61.26 2.50

10 0.00093 0.00462 92,300 426 460,434 0.99401 5,229,136 56.65 2.50

15 0.00161 0.00802 91,874 737 457,676 0.98982 4,768,702 51.91 2.70

20 0.00251 0.01247 91,137 1,136 453,018 0.98510 4,311,026 47.30 2.65

25 0.00346 0.01717 90,000 1,545 446,266 0.98191 3,858,009 42.87 2.58

30 0.00387 0.01918 88,455 1,697 438,193 0.97679 3,411,742 38.57 2.59

35 0.00566 0.02794 86,758 2,424 428,023 0.96805 2,973,549 34.27 2.62

40 0.00730 0.03585 84,334 3,023 414,348 0.96073 2,545,526 30.18 2.58

45 0.00885 0.04331 81,311 3,522 398,076 0.94894 2,131,178 26.21 2.59

50 0.01242 0.06031 77,789 4,691 377,749 0.92952 1,733,101 22.28 2.61

55 0.01724 0.08282 73,098 6,054 351,125 0.89705 1,355,353 18.54 2.63

60 0.02717 0.12766 67,044 8,559 314,975 0.84142 1,004,228 14.98 2.63

65 0.04318 0.19566 58,485 11,443 265,026 0.75848 689,253 11.79 2.61

70 0.06947 0.29684 47,042 13,964 201,017 0.64253 424,227 9.02 2.55

75 0.11067 0.43215 33,078 14,295 129,161 0.42135 223,210 6.75 2.47

80 0.19972 ... 18,783 18,783 94,050 ... 94,050 5.01 5.01




Females Rakhine

0 0.05218 0.05009 100,000 5,009 95,994 0.94809 6,926,006 69.26 0.20

1 0.00198 0.00787 94,991 748 378,052 0.99165 6,830,011 71.90 1.44

5 0.00096 0.00480 94,243 452 470,086 0.99543 6,451,959 68.46 2.50

10 0.00087 0.00433 93,791 406 467,940 0.99476 5,981,873 63.78 2.50

15 0.00126 0.00630 93,385 588 465,487 0.99393 5,513,933 59.05 2.56

20 0.00116 0.00576 92,797 535 462,662 0.99354 5,048,446 54.40 2.53

25 0.00146 0.00729 92,262 673 459,672 0.99246 4,585,784 49.70 2.56

30 0.00159 0.00792 91,590 725 456,207 0.99027 4,126,112 45.05 2.60

35 0.00241 0.01200 90,864 1,090 451,768 0.98533 3,669,905 40.39 2.66

40 0.00349 0.01731 89,774 1,554 445,141 0.98139 3,218,137 35.85 2.60

45 0.00406 0.02012 88,220 1,775 436,856 0.97528 2,772,996 31.43 2.61

50 0.00615 0.03029 86,445 2,618 426,058 0.96412 2,336,140 27.02 2.65

55 0.00867 0.04249 83,826 3,562 410,772 0.94635 1,910,083 22.79 2.65

60 0.01397 0.06764 80,265 5,429 388,734 0.91217 1,499,310 18.68 2.68

65 0.02380 0.11279 74,835 8,441 354,593 0.85205 1,110,576 14.84 2.68

70 0.04200 0.19112 66,395 12,689 302,131 0.75420 755,983 11.39 2.65

75 0.07380 0.31311 53,705 16,816 227,866 0.49793 453,851 8.45 2.58

80 0.16324 ... 36,890 36,890 225,985 ... 225,985 6.13 6.13

Yangon

Both sexes Yangon

0 0.04273 0.04125 100,000 4,125 96,540 0.95746 6,553,032 65.53 0.16

1 0.00139 0.00553 95,875 530 382,190 0.99408 6,456,492 67.34 1.53

5 0.00070 0.00348 95,345 332 475,895 0.99708 6,074,302 63.71 2.50

10 0.00047 0.00236 95,013 224 474,504 0.99718 5,598,408 58.92 2.50

15 0.00071 0.00355 94,789 337 473,166 0.99542 5,123,903 54.06 2.69

20 0.00118 0.00587 94,452 554 471,000 0.99200 4,650,737 49.24 2.72

25 0.00212 0.01053 93,898 989 467,234 0.98631 4,179,737 44.51 2.72

30 0.00344 0.01705 92,909 1,584 460,836 0.97991 3,712,504 39.96 2.66

35 0.00468 0.02313 91,325 2,112 451,578 0.97336 3,251,668 35.61 2.61

40 0.00613 0.03018 89,213 2,692 439,546 0.96713 2,800,090 31.39 2.58

45 0.00728 0.03576 86,520 3,094 425,099 0.95960 2,360,544 27.28 2.58

50 0.00946 0.04625 83,426 3,858 407,927 0.94461 1,935,445 23.20 2.61

55 0.01386 0.06710 79,568 5,339 385,333 0.91385 1,527,518 19.20 2.66

60 0.02315 0.10983 74,229 8,153 352,135 0.85882 1,142,185 15.39 2.67

65 0.03918 0.17934 66,076 11,850 302,421 0.77102 790,050 11.96 2.64

70 0.06724 0.28911 54,226 15,677 233,171 0.64499 487,630 8.99 2.58

75 0.11199 0.43692 38,549 16,843 150,392 0.40897 254,458 6.60 2.49

80 0.20858 ... 21,706 21,706 104,066 ... 104,066 4.79 4.79




Males Yangon

0 0.04947 0.04754 100,000 4,754 96,098 0.95104 6,053,365 60.53 0.18

1 0.00165 0.00659 95,246 628 379,421 0.99313 5,957,267 62.55 1.51

5 0.00071 0.00355 94,618 336 472,252 0.99686 5,577,846 58.95 2.50

10 0.00055 0.00273 94,282 257 470,769 0.99655 5,105,594 54.15 2.50

15 0.00092 0.00457 94,025 430 469,143 0.99407 4,634,826 49.29 2.71

20 0.00155 0.00770 93,595 721 466,362 0.98858 4,165,682 44.51 2.76

25 0.00323 0.01601 92,875 1,487 461,037 0.97866 3,699,321 39.83 2.76

30 0.00546 0.02695 91,388 2,463 451,199 0.96766 3,238,284 35.43 2.67

35 0.00769 0.03774 88,925 3,356 436,606 0.95655 2,787,085 31.34 2.61

40 0.01003 0.04897 85,569 4,190 417,637 0.94748 2,350,479 27.47 2.56

45 0.01156 0.05619 81,379 4,573 395,703 0.93778 1,932,842 23.75 2.55

50 0.01452 0.07017 76,806 5,389 371,082 0.91614 1,537,139 20.01 2.60

55 0.02135 0.10164 71,416 7,259 339,962 0.87065 1,166,057 16.33 2.64

60 0.03545 0.16356 64,158 10,494 295,986 0.79413 826,095 12.88 2.64

65 0.05860 0.25666 53,664 13,773 235,051 0.68466 530,109 9.88 2.58

70 0.09558 0.38560 39,891 15,382 160,930 0.54774 295,058 7.40 2.50

75 0.14915 0.53645 24,509 13,148 88,148 0.34281 134,129 5.47 2.38

80 0.24708 ... 11,361 11,361 45,981 ... 45,981 4.05 4.05

Females Yangon

0 0.03573 0.03468 100,000 3,468 97,066 0.96415 7,079,691 70.80 0.15

1 0.00115 0.00458 96,532 442 385,008 0.99493 6,982,625 72.33 1.47

5 0.00068 0.00341 96,090 328 479,630 0.99730 6,597,617 68.66 2.50

10 0.00040 0.00199 95,762 191 478,335 0.99779 6,117,986 63.89 2.50

15 0.00052 0.00259 95,572 248 477,279 0.99662 5,639,652 59.01 2.66

20 0.00086 0.00429 95,324 409 475,666 0.99497 5,162,373 54.16 2.67

25 0.00116 0.00578 94,915 549 473,275 0.99313 4,686,707 49.38 2.63

30 0.00161 0.00804 94,367 759 470,022 0.99098 4,213,432 44.65 2.61

35 0.00203 0.01010 93,608 945 465,782 0.98808 3,743,410 39.99 2.61

40 0.00282 0.01400 92,662 1,297 460,228 0.98385 3,277,628 35.37 2.62

45 0.00376 0.01861 91,365 1,700 452,793 0.97768 2,817,401 30.84 2.63

50 0.00541 0.02673 89,665 2,397 442,685 0.96760 2,364,608 26.37 2.65

55 0.00804 0.03945 87,268 3,443 428,343 0.94865 1,921,923 22.02 2.68

60 0.01372 0.06651 83,825 5,575 406,347 0.91063 1,493,580 17.82 2.71

65 0.02499 0.11817 78,250 9,247 370,034 0.83971 1,087,233 13.89 2.71

70 0.04723 0.21268 69,003 14,676 310,720 0.72120 717,199 10.39 2.66

75 0.08763 0.36147 54,328 19,638 224,092 0.44870 406,479 7.48 2.58

80 0.1902 ... 34,690 34,690 182,387 ... 182,387 5.26 5.26




Shan

Both sexes Shan

0 0.05346 0.05124 100,000 5,124 95,849 0.94701 6,474,745 64.75 0.19

1 0.00195 0.00778 94,876 738 377,658 0.99112 6,378,896 67.23 1.50

5 0.00118 0.00590 94,138 555 469,301 0.99421 6,001,239 63.75 2.50

10 0.00114 0.00567 93,582 531 466,586 0.99389 5,531,938 59.11 2.50

15 0.00136 0.00678 93,052 631 463,734 0.99242 5,065,352 54.44 2.58

20 0.00171 0.00852 92,421 787 460,219 0.99018 4,601,618 49.79 2.61

25 0.00229 0.01141 91,634 1,046 455,700 0.98594 4,141,399 45.20 2.64

30 0.00343 0.01703 90,588 1,543 449,293 0.98016 3,685,699 40.69 2.64

35 0.00457 0.02259 89,045 2,012 440,381 0.97500 3,236,405 36.35 2.59

40 0.00556 0.02745 87,034 2,389 429,370 0.96981 2,796,025 32.13 2.57

45 0.00685 0.03372 84,645 2,854 416,407 0.95893 2,366,654 27.96 2.61

50 0.01019 0.04976 81,790 4,070 399,304 0.94173 1,950,247 23.84 2.63

55 0.01413 0.06837 77,721 5,314 376,038 0.91440 1,550,943 19.96 2.64

60 0.02249 0.10678 72,407 7,732 343,850 0.86633 1,174,905 16.23 2.65

65 0.03603 0.16595 64,675 10,733 297,889 0.79259 831,055 12.85 2.63

70 0.05883 0.25748 53,942 13,889 236,105 0.68507 533,166 9.88 2.58

75 0.09541 0.38530 40,053 15,433 161,749 0.45550 297,061 7.42 2.50

80 0.18195 ... 24,621 24,621 135,313 ... 135,313 5.50 5.50

Males Shan

0 0.05981 0.05710 100,000 5,710 95,470 0.94108 6,054,416 60.54 0.21

1 0.00221 0.00879 94,290 829 375,072 0.98996 5,958,946 63.20 1.48

5 0.00128 0.00636 93,461 594 465,820 0.99346 5,583,874 59.75 2.50

10 0.00135 0.00673 92,867 625 462,771 0.99242 5,118,054 55.11 2.50

15 0.00176 0.00878 92,242 810 459,265 0.99020 4,655,282 50.47 2.60

20 0.00221 0.01101 91,432 1,007 454,763 0.98678 4,196,018 45.89 2.62

25 0.00320 0.01590 90,425 1,438 448,751 0.98053 3,741,254 41.37 2.65

30 0.00475 0.02350 88,987 2,091 440,012 0.97170 3,292,503 37.00 2.64

35 0.00673 0.03312 86,896 2,878 427,559 0.96378 2,852,491 32.83 2.59

40 0.00801 0.03927 84,018 3,299 412,073 0.95604 2,424,932 28.86 2.57

45 0.01020 0.04978 80,719 4,018 393,961 0.94087 2,012,859 24.94 2.60

50 0.01450 0.07007 76,701 5,374 370,666 0.91816 1,618,898 21.11 2.61

55 0.02013 0.09603 71,326 6,849 340,331 0.88111 1,248,232 17.50 2.62

60 0.03157 0.14685 64,477 9,468 299,871 0.81906 907,901 14.08 2.62

65 0.04963 0.22162 55,008 12,191 245,612 0.72999 608,031 11.05 2.59

70 0.07836 0.32812 42,817 14,049 179,294 0.61139 362,418 8.46 2.52

75 0.12164 0.46348 28,768 13,333 109,618 0.40140 183,124 6.37 2.43

80 0.20998 ... 15,435 15,435 73,506 ... 73,506 4.76 4.76




Females Shan

0 0.04713 0.04539 100,000 4,539 96,306 0.95307 6,938,750 69.39 0.19

1 0.00166 0.00663 95,461 633 380,230 0.99226 6,842,444 71.68 1.45

5 0.00109 0.00546 94,828 518 472,846 0.99491 6,462,214 68.15 2.50

10 0.00094 0.00471 94,310 444 470,441 0.99524 5,989,368 63.51 2.50

15 0.00099 0.00492 93,866 462 468,202 0.99449 5,518,927 58.80 2.56

20 0.00124 0.00619 93,404 578 465,620 0.99340 5,050,725 54.07 2.58

25 0.00143 0.00714 92,826 663 462,546 0.99124 4,585,105 49.39 2.61

30 0.00212 0.01054 92,163 971 458,493 0.98861 4,122,560 44.73 2.61

35 0.00245 0.01220 91,192 1,113 453,273 0.98589 3,664,067 40.18 2.58

40 0.00326 0.01619 90,079 1,458 446,876 0.98265 3,210,794 35.64 2.59

45 0.00382 0.01895 88,621 1,679 439,123 0.97562 2,763,918 31.19 2.63

50 0.00628 0.03096 86,942 2,692 428,418 0.96316 2,324,796 26.74 2.66

55 0.00890 0.04360 84,250 3,673 412,633 0.94483 1,896,378 22.51 2.65

60 0.01440 0.06968 80,577 5,615 389,870 0.90936 1,483,745 18.41 2.68

65 0.02465 0.11657 74,962 8,738 354,533 0.84698 1,093,875 14.59 2.68

70 0.04361 0.19775 66,224 13,096 300,284 0.74611 739,342 11.16 2.65

75 0.07661 0.32308 53,128 17,165 224,044 0.48972 439,059 8.26 2.58

80 0.16726 ... 35,963 35,963 215,015 ... 215,015 5.98 5.98

Ayeyawady

Both sexes Ayeyawady

0 0.08987 0.08445 100,000 8,445 93,964 0.91160 6,363,885 63.64 0.29

1 0.00466 0.01841 91,555 1,686 361,835 0.98400 6,269,921 68.48 1.40

5 0.00075 0.00374 89,869 336 448,507 0.99656 5,908,085 65.74 2.50

10 0.00063 0.00313 89,533 280 446,966 0.99598 5,459,578 60.98 2.50

15 0.00105 0.00524 89,253 468 445,171 0.99399 5,012,612 56.16 2.66

20 0.00136 0.00678 88,785 602 442,495 0.99198 4,567,441 51.44 2.62

25 0.00190 0.00945 88,183 833 438,946 0.98878 4,124,947 46.78 2.63

30 0.00265 0.01316 87,350 1,150 434,021 0.98465 3,686,000 42.20 2.63

35 0.00356 0.01765 86,201 1,521 427,358 0.98003 3,251,979 37.73 2.60

40 0.00453 0.02241 84,679 1,898 418,823 0.97478 2,824,621 33.36 2.59

45 0.00575 0.02834 82,782 2,346 408,260 0.96785 2,405,798 29.06 2.59

50 0.00748 0.03676 80,435 2,957 395,134 0.95604 1,997,538 24.83 2.62

55 0.01091 0.05317 77,479 4,120 377,763 0.93168 1,602,404 20.68 2.66

60 0.01819 0.08728 73,359 6,403 351,955 0.88639 1,224,641 16.69 2.68

65 0.03135 0.14607 66,956 9,780 311,968 0.80931 872,686 13.03 2.67

70 0.05558 0.24545 57,176 14,034 252,479 0.68915 560,718 9.81 2.62

75 0.09719 0.39199 43,142 16,911 173,995 0.43552 308,240 7.14 2.53

80 0.19540 ... 26,231 26,231 134,245 ... 134,245 5.12 5.12




Males Ayeyawady

0 0.10391 0.09707 100,000 9,707 93,415 0.89922 6,018,370 60.18 0.32

1 0.00530 0.02089 90,293 1,886 356,193 0.98132 5,924,955 65.62 1.36

5 0.00075 0.00373 88,407 330 441,210 0.99650 5,568,762 62.99 2.50

10 0.00065 0.00326 88,077 287 439,667 0.99563 5,127,552 58.22 2.50

15 0.00118 0.00590 87,790 518 437,747 0.99319 4,687,885 53.40 2.68

20 0.00156 0.00776 87,272 677 434,768 0.99021 4,250,138 48.70 2.65

25 0.00246 0.01222 86,595 1,058 430,510 0.98496 3,815,370 44.06 2.67

30 0.00364 0.01804 85,537 1,543 424,035 0.97889 3,384,861 39.57 2.64

35 0.00489 0.02419 83,993 2,032 415,085 0.97296 2,960,826 35.25 2.60

40 0.00609 0.03003 81,962 2,461 403,859 0.96615 2,545,741 31.06 2.58

45 0.00775 0.03805 79,500 3,025 390,188 0.95757 2,141,882 26.94 2.58

50 0.00978 0.04778 76,475 3,654 373,632 0.94295 1,751,693 22.91 2.61

55 0.01427 0.06906 72,821 5,029 352,318 0.91143 1,378,061 18.92 2.66

60 0.02383 0.11287 67,792 7,652 321,111 0.85487 1,025,744 15.13 2.67

65 0.04041 0.18445 60,141 11,093 274,510 0.76462 704,632 11.72 2.64

70 0.06948 0.29734 49,048 14,584 209,897 0.63394 430,122 8.77 2.58

75 0.11697 0.45161 34,464 15,564 133,062 0.39579 220,226 6.39 2.48

80 0.21683 ... 18,900 18,900 87,163 ... 87,163 4.61 4.61

Females Ayeyawady

0 0.07596 0.07195 100,000 7,195 94,718 0.92457 6,720,066 67.20 0.27

1 0.00383 0.01517 92,805 1,408 367,569 0.98668 6,625,348 71.39 1.41

5 0.00075 0.00375 91,397 343 456,129 0.99662 6,257,778 68.47 2.50

10 0.00060 0.00300 91,054 273 454,589 0.99633 5,801,650 63.72 2.50

15 0.00092 0.00459 90,781 417 452,922 0.99474 5,347,060 58.90 2.64

20 0.00118 0.00588 90,365 531 450,539 0.99361 4,894,138 54.16 2.58

25 0.00139 0.00692 89,833 622 447,660 0.99229 4,443,599 49.46 2.58

30 0.00174 0.00865 89,212 772 444,210 0.98994 3,995,939 44.79 2.61

35 0.00235 0.01166 88,440 1,031 439,742 0.98647 3,551,729 40.16 2.62

40 0.00312 0.01550 87,409 1,355 433,794 0.98261 3,111,987 35.60 2.60

45 0.00394 0.01951 86,054 1,679 426,251 0.97722 2,678,193 31.12 2.61

50 0.00541 0.02673 84,375 2,255 416,541 0.96783 2,251,942 26.69 2.63

55 0.00795 0.03901 82,120 3,203 403,141 0.94955 1,835,401 22.35 2.67

60 0.01340 0.06498 78,916 5,128 382,802 0.91327 1,432,261 18.15 2.70

65 0.02407 0.11405 73,788 8,416 349,602 0.84613 1,049,459 14.22 2.70

70 0.04493 0.20333 65,373 13,292 295,810 0.73356 699,857 10.71 2.66

75 0.08283 0.34510 52,080 17,973 216,995 0.46295 404,047 7.76 2.58

80 0.18234 ... 34,107 34,107 187,052 ... 187,052 5.48 5.48




Nay Pyi Taw

Both sexes Nay Pyi Taw

0 0.05645 0.05400 100,000 5,400 95,668 0.94409 6,766,170 67.66 0.20

1 0.00214 0.00853 94,600 807 376,375 0.99126 6,670,502 70.51 1.49

5 0.00090 0.00448 93,793 420 467,915 0.99664 6,294,127 67.11 2.50

10 0.00045 0.00224 93,373 209 466,341 0.99650 5,826,212 62.40 2.50

15 0.00104 0.00520 93,164 484 464,709 0.99419 5,359,871 57.53 2.71

20 0.00124 0.00619 92,679 574 462,011 0.99314 4,895,162 52.82 2.58

25 0.00158 0.00789 92,106 727 458,843 0.98891 4,433,151 48.13 2.68

30 0.00301 0.01497 91,379 1,368 453,754 0.98144 3,974,308 43.49 2.70

35 0.00436 0.02155 90,011 1,940 445,333 0.97837 3,520,554 39.11 2.57

40 0.00432 0.02137 88,071 1,882 435,699 0.97691 3,075,221 34.92 2.53

45 0.00514 0.02538 86,189 2,187 425,638 0.97173 2,639,522 30.62 2.57

50 0.00648 0.03191 84,002 2,680 413,604 0.96206 2,213,884 26.36 2.61

55 0.00933 0.04567 81,321 3,714 397,910 0.94191 1,800,280 22.14 2.66

60 0.01523 0.07356 77,607 5,709 374,794 0.90479 1,402,370 18.07 2.68

65 0.02584 0.12189 71,898 8,764 339,109 0.84064 1,027,576 14.29 2.67

70 0.04551 0.20548 63,135 12,973 285,071 0.73633 688,467 10.90 2.64

75 0.08015 0.33541 50,162 16,825 209,905 0.47966 403,396 8.04 2.57

80 0.17229 ... 33,337 33,337 193,491 ... 193,491 5.80 5.80

Males Nay Pyi Taw

0 0.06601 0.06279 100,000 6,279 95,121 0.93513 6,367,931 63.68 0.22

1 0.00258 0.01026 93,721 962 372,445 0.98957 6,272,810 66.93 1.46

5 0.00096 0.00477 92,759 442 462,691 0.99669 5,900,364 63.61 2.50

10 0.00037 0.00184 92,317 170 461,160 0.99699 5,437,673 58.90 2.50

15 0.00094 0.00468 92,147 431 459,773 0.99428 4,976,513 54.01 2.77

20 0.00134 0.00668 91,716 613 457,143 0.99197 4,516,741 49.25 2.66

25 0.00200 0.00998 91,103 909 453,474 0.98410 4,059,598 44.56 2.75

30 0.00463 0.02291 90,194 2,066 446,262 0.97296 3,606,124 39.98 2.72

35 0.00608 0.02996 88,128 2,640 434,193 0.96876 3,159,862 35.86 2.56

40 0.00653 0.03213 85,487 2,747 420,627 0.96654 2,725,669 31.88 2.52

45 0.00717 0.03524 82,741 2,916 406,552 0.96143 2,305,042 27.86 2.55

50 0.00880 0.04311 79,825 3,441 390,871 0.94872 1,898,491 23.78 2.60

55 0.01276 0.06195 76,384 4,732 370,829 0.92065 1,507,619 19.74 2.66

60 0.02120 0.10103 71,652 7,239 341,403 0.86984 1,136,791 15.87 2.67

65 0.03596 0.16577 64,413 10,678 296,967 0.78689 795,387 12.35 2.65

70 0.06232 0.27101 53,735 14,563 233,682 0.66170 498,420 9.28 2.60

75 0.10685 0.42177 39,172 16,522 154,628 0.41592 264,738 6.76 2.50

80 0.20571 ... 22,651 22,651 110,110 ... 110,110 4.86 4.86




Females Nay Pyi Taw

0 0.04685 0.04513 100,000 4,513 96,324 0.95332 7,156,139 71.56 0.19

1 0.00166 0.00662 95,487 632 380,336 0.99291 7,059,815 73.93 1.45

5 0.00084 0.00419 94,855 397 473,281 0.99658 6,679,479 70.42 2.50

10 0.00053 0.00264 94,457 249 471,664 0.99602 6,206,198 65.70 2.50

15 0.00114 0.00568 94,208 535 469,788 0.99411 5,734,534 60.87 2.66

20 0.00116 0.00576 93,673 540 467,021 0.99416 5,264,746 56.20 2.51

25 0.00121 0.00602 93,133 561 464,296 0.99341 4,797,725 51.51 2.55

30 0.00152 0.00755 92,573 699 461,236 0.98930 4,333,429 46.81 2.67

35 0.00279 0.01384 91,874 1,272 456,300 0.98705 3,872,193 42.15 2.59

40 0.00236 0.01173 90,602 1,063 450,391 0.98614 3,415,893 37.70 2.53

45 0.00337 0.01671 89,540 1,496 444,147 0.98071 2,965,502 33.12 2.63

50 0.00449 0.02219 88,043 1,954 435,577 0.97360 2,521,355 28.64 2.63

55 0.00643 0.03169 86,090 2,728 424,080 0.95947 2,085,777 24.23 2.67

60 0.01059 0.05171 83,361 4,311 406,891 0.93159 1,661,698 19.93 2.70

65 0.01863 0.08934 79,051 7,062 379,056 0.87958 1,254,807 15.87 2.71

70 0.03439 0.15926 71,988 11,465 333,412 0.78760 875,751 12.17 2.69

75 0.06415 0.27831 60,524 16,844 262,594 0.51581 542,339 8.96 2.62

80 0.15614 ... 43,679 43,679 279,745 ... 279,745 6.40 6.40












Appendix F. Life expectancy at birth by Districts

The life expectancies were calculated by first computing life tables for the Districts using the programme MATCH from the demographic package MORTPAK, Version 4.3 (UNPD, 2013). Then life expectancy at birth from the life tables was generated. This programme (MATCH) calculates United Nations and Coale-Demeny model life tables or user-designed model life tables corresponding to given levels of mortality.

The input of this programme is the value of any life table column for any age. This input specifies the level of the model life table. In fact, any life table function for any age defines unequivocally a model life table. Hence, the output is a life table model corresponding to the imputed level. When a user designed or empirical life table is used instead of a model, the life table produced corresponds to the mortality pattern of the population from which the empirical life table was obtained.

In the case of Myanmar, the user-designed or empirical life table was calculated for the State/Region, and the level was the infant mortality estimated for the District. In other words, the level corresponds to the District, but the pattern to the State/Region. The assumption of this approach is that the pattern of mortality in the Districts is the pattern of the State/Region to which they belong.

The life expectancy at birth for each District was then extracted from the life table and is presented in the table below.

Table FLife expectancy at birth by State/Region and District, 2014 Census

State/Region and DistrictLife expectancy at birth in years

Both sexes Males Females

Kachin 64.23 59.36 69.31

Myitkyina 64.26 59.42 69.30

Mohnyin 64.98 60.14 70.13

Bhamo 64.44 59.44 68.44

Putao 60.18 55.42 64.51

Kayah 64.28 59.10 70.22

Loikaw 64.41 59.19 68.77

Bawlakhe 63.40 58.40 69.01

Kayin 62.08 57.74 66.72

Hpa-An 61.72 57.31 66.38

Pharpon 60.78 56.64 64.93

Myawady 65.38 61.15 70.50

Kawkareik 61.11 56.77 65.53

Chin 60.48 57.37 63.49

Hakha 72.59 70.69 76.46

Falam 65.31 62.64 68.73

Mindat 53.70 50.17 55.71




Sagaing 65.75 60.96 70.43

Sagaing 70.00 65.38 75.13

Shwebo 79.36 75.99 84.57

Monywa 68.57 64.26 73.10

Katha 63.41 65.26 67.89

Kalay 65.45 60.44 70.37

Tamu 67.30 63.52 71.07

Mawlaik 61.82 57.55 65.22

Hkamti 61.95 57.05 66.14

Yinmarpin 64.27 59.01 69.28

Tanintharyi 65.53 62.20 68.90

Dawei 69.33 66.59 72.00

Myeik 64.28 60.95 67.69

Kawthoung 64.09 59.98 68.28

Bago 65.20 60.72 69.75

Bago 66.08 61.60 70.76

Taungoo 63.77 59.20 68.16

Pyay 66.34 62.05 70.84

Thayawady 64.41 59.88 68.86

Magway 62.27 57.08 67.49

Magway 63.11 64.41 68.25

Minbu 62.41 57.28 67.58

Thayet 63.37 58.30 68.66

Pakokku 60.04 54.68 65.17

Gangaw 64.21 58.65 70.11

Mandalay 64.89 59.68 70.17

Mandalay 70.08 64.52 74.78

Pyin oo lwin 66.00 59.78 70.42

Kyaukse 66.52 60.29 71.07

Myingyan 64.50 58.30 68.46

Nyaung U 61.75 54.94 65.68

Yame`thin 61.53 54.61 65.52

Meiktila 63.32 56.96 67.17

Mon 63.50 58.24 69.07

Mawlamyine 65.45 60.22 71.26

Thaton 61.17 55.86 66.39

Rakhine 65.47 61.60 69.26

Sittwe 68.43 64.56 72.78

Myauk U 63.28 59.22 66.85

Maungtaw 66.35 63.53 69.04

Kyaukpyu 64.60 60.72 68.23

Thandwe 66.57 62.80 70.47

Appendix F. Life expectancy at birth by State/Region and District, 2014 Census




Yangon 65.53 60.53 70.80

North 64.20 58.99 69.52

East 68.79 64.09 74.18

South 62.87 57.65 67.98

West 70.94 67.05 75.64

Shan 64.75 60.54 69.39

Taunggyi 63.33 58.54 68.47

Loilin 63.45 59.24 67.84

Linkhe 60.52 56.15 64.59

Lashio 65.35 61.24 69.95

Muse 68.78 61.24 73.35

Kyaukme 61.57 57.34 65.65

Kunlon 70.05 66.17 75.01

Laukine 75.78 72.37 81.16

Hopan 74.07 71.20 78.56

Makman 63.78 59.26 67.64

Kengtaung 67.81 63.06 72.73

Minesat 60.34 55.38 63.90

Tachileik 67.97 63.45 72.66

Minephyat 65.65 60.60 70.50

Ayeyawady 63.64 60.18 67.20

Pathein 63.38 60.48 66.14

Phyapon 58.27 55.25 60.24

Maubin 62.31 59.32 65.01

Myaungmya 60.81 57.96 63.03

Labutta 52.87 50.08 53.30

Hinthada 61.99 58.72 65.01

Nay Pyi Taw 67.66 63.68 71.56

Ottara 68.00 63.97 72.03

Dekkhina 67.11 63.18 70.80

Appendix F. Life expectancy at birth by State/Region and District, 2014 Census


List of ContributorsContributors to the Mortality thematic report

NAME INSTITUTION ROLE

Government Coordination

U Myint Kyaing Permanent Secretary, Ministry of Labour, Immigration and Population

Overall administration and coordination

U Nyi Nyi Director, Department of Population (DOP)

Administration, coordination and quality assurance

Daw Khaing Khaing Soe Director, DOP Administration, coordination and quality assurance

UNFPA Coordination

Janet E. Jackson UNFPA Country Representative Overall administration and coordination

Fredrick Okwayo Chief Technical Advisor Overall design, administration, coordination and quality assurance

Petra Righetti Census Donor Coordinator Administration and coordination

Thet Thet U Programme Assistant Administration and logistics

Tun Tun Win Project Assistant Administration and logistics

Authors

Ricardo Neupert UNFPA Consultant Lead Author

U Nyi Nyi Director, DOP Assisting Author

Daw Yin Yin Kyaing Deputy Director, DOP Trainee and Assisting Author

Daw Thi Thi Nwe Assistant Director, DOP Trainee and Assisting Author

Daw Aye Theingi Win Staff Officer, DOP Trainee and Assisting Author

Daw Myo Thandar Staff Officer, DOP Trainee and Assisting Author

Daw Wint No No Tun Junior Clerk, DOP Trainee and Assisting Author

Reviewers and Editors

Ian Stuart White UNFPA Consultant Editing and reviewing

Fredrick Okwayo UNFPA Chief Technical Advisor Proof reading, editing and reviewing

Daniel Msonda UNFPA Census Consultant Proof reading, editing and reviewing

Pedro Andres Montes UNFPA Census Consultant Editing and reviewing

Esther Bayliss UNFPA Communications Consultant Proof reading, editing and reviewing

U Nyi Nyi Director, DOP Proof reading, editing and reviewing

Daw Khaing Khaing Soe Director, DOP Proof reading, editing and reviewing

Gouranga Dasvarma UNFPA Consultant Technical peer review

Thomas Spoorenberg United Nations Statistics Division Technical peer review

Data Processing and IT Team

Arij Dekker UNFPA Data Processing Consultant Data editing and programming

Daw Khaing Khaing Soe Director, DOP Programming and generation of tables

Daw Sandar Myint Deputy Director, DOP Programming and generation of tables

Daw May Myint Bo Staff Officer, DOP Generation of tables

Daw Lin Lin Mar Staff Officer, DOP Generation of maps

Daw Su Myat Oo Immigration Assistant, DOP Generation of tables

Daw Aye Thiri Zaw Junior Clerk, DOP Generation of tables

U Thant Zin Oo Assistant Computer Operator, DOP Generation of maps

U Wai Phyo Win UNFPA Census IT Manager Information technology services

Designer

Karlien Truyens UNFPA Consultant Graphic Designer

Thematic Report on Mortality can be downloaded at:

www.dop.gov.mm

or

http://myanmar.unfpa.org/census

Documents

Department of Population - The Republic of the Union of Myanmar … · 2019-02-15 · II Census Report Volume 4-B Mortalit Foreword The 2014 Myanmar Population and Housing Census